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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.17213">arXiv:2502.17213</a> <span> [<a href="https://arxiv.org/pdf/2502.17213">pdf</a>, <a href="https://arxiv.org/format/2502.17213">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+J">Jiahe Li</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+X">Xin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+F">Fanqi Shen</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junru Chen</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Y">Yuxin Liu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+D">Daoze Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Yuan%2C+Z">Zhizhang Yuan</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+F">Fang Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Meng Li</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Y">Yang Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.17213v1-abstract-short" style="display: inline;"> Neurological disorders represent significant global health challenges, driving the advancement of brain signal analysis methods. Scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) are widely used to diagnose and monitor neurological conditions. However, dataset heterogeneity and task variations pose challenges in developing robust deep learning solutions. This review… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17213v1-abstract-full').style.display = 'inline'; document.getElementById('2502.17213v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17213v1-abstract-full" style="display: none;"> Neurological disorders represent significant global health challenges, driving the advancement of brain signal analysis methods. Scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) are widely used to diagnose and monitor neurological conditions. However, dataset heterogeneity and task variations pose challenges in developing robust deep learning solutions. This review systematically examines recent advances in deep learning approaches for EEG/iEEG-based neurological diagnostics, focusing on applications across 7 neurological conditions using 46 datasets. We explore trends in data utilization, model design, and task-specific adaptations, highlighting the importance of pre-trained multi-task models for scalable, generalizable solutions. To advance research, we propose a standardized benchmark for evaluating models across diverse datasets to enhance reproducibility. This survey emphasizes how recent innovations can transform neurological diagnostics and enable the development of intelligent, adaptable healthcare solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17213v1-abstract-full').style.display = 'none'; document.getElementById('2502.17213v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12186">arXiv:2502.12186</a> <span> [<a href="https://arxiv.org/pdf/2502.12186">pdf</a>, <a href="https://arxiv.org/format/2502.12186">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> E2CB2former: Effecitve and Explainable Transformer for CB2 Receptor Ligand Activity Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xie%2C+J">Jiacheng Xie</a>, <a href="/search/q-bio?searchtype=author&query=Ji%2C+Y">Yingrui Ji</a>, <a href="/search/q-bio?searchtype=author&query=Zeng%2C+L">Linghuan Zeng</a>, <a href="/search/q-bio?searchtype=author&query=Xiao%2C+X">Xi Xiao</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Gaofei Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+L">Lijing Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Mondal%2C+J+J">Joyanta Jyoti Mondal</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiansheng Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12186v1-abstract-short" style="display: inline;"> Accurate prediction of CB2 receptor ligand activity is pivotal for advancing drug discovery targeting this receptor, which is implicated in inflammation, pain management, and neurodegenerative conditions. Although conventional machine learning and deep learning techniques have shown promise, their limited interpretability remains a significant barrier to rational drug design. In this work, we intr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12186v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12186v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12186v1-abstract-full" style="display: none;"> Accurate prediction of CB2 receptor ligand activity is pivotal for advancing drug discovery targeting this receptor, which is implicated in inflammation, pain management, and neurodegenerative conditions. Although conventional machine learning and deep learning techniques have shown promise, their limited interpretability remains a significant barrier to rational drug design. In this work, we introduce CB2former, a framework that combines a Graph Convolutional Network with a Transformer architecture to predict CB2 receptor ligand activity. By leveraging the Transformer's self attention mechanism alongside the GCN's structural learning capability, CB2former not only enhances predictive performance but also offers insights into the molecular features underlying receptor activity. We benchmark CB2former against diverse baseline models including Random Forest, Support Vector Machine, K Nearest Neighbors, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, Convolutional Neural Network, and Recurrent Neural Network and demonstrate its superior performance with an R squared of 0.685, an RMSE of 0.675, and an AUC of 0.940. Moreover, attention weight analysis reveals key molecular substructures influencing CB2 receptor activity, underscoring the model's potential as an interpretable AI tool for drug discovery. This ability to pinpoint critical molecular motifs can streamline virtual screening, guide lead optimization, and expedite therapeutic development. Overall, our results showcase the transformative potential of advanced AI approaches exemplified by CB2former in delivering both accurate predictions and actionable molecular insights, thus fostering interdisciplinary collaboration and innovation in drug discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12186v1-abstract-full').style.display = 'none'; document.getElementById('2502.12186v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.07297">arXiv:2502.07297</a> <span> [<a href="https://arxiv.org/pdf/2502.07297">pdf</a>, <a href="https://arxiv.org/format/2502.07297">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Shao%2C+Q">Qian Shao</a>, <a href="/search/q-bio?searchtype=author&query=Du%2C+B">Bang Du</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Z">Zepeng Li</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Q">Qiyuan Chen</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+H">Hongxia Xu</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+J">Jimeng Sun</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+J">Jian Wu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jintai Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.07297v1-abstract-short" style="display: inline;"> Clinical trials are pivotal in cardiac drug development, yet they often fail due to inadequate efficacy and unexpected safety issues, leading to significant financial losses. Using in-silico trials to replace a part of physical clinical trials, e.g., leveraging advanced generative models to generate drug-influenced electrocardiograms (ECGs), seems an effective method to reduce financial risk and p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07297v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07297v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07297v1-abstract-full" style="display: none;"> Clinical trials are pivotal in cardiac drug development, yet they often fail due to inadequate efficacy and unexpected safety issues, leading to significant financial losses. Using in-silico trials to replace a part of physical clinical trials, e.g., leveraging advanced generative models to generate drug-influenced electrocardiograms (ECGs), seems an effective method to reduce financial risk and potential harm to trial participants. While existing generative models have demonstrated progress in ECG generation, they fall short in modeling drug reactions due to limited fidelity and inability to capture individualized drug response patterns. In this paper, we propose a Drug-Aware Diffusion Model (DADM), which could simulate individualized drug reactions while ensuring fidelity. To ensure fidelity, we construct a set of ordinary differential equations to provide external physical knowledge (EPK) of the realistic ECG morphology. The EPK is used to adaptively constrain the morphology of the generated ECGs through a dynamic cross-attention (DCA) mechanism. Furthermore, we propose an extension of ControlNet to incorporate demographic and drug data, simulating individual drug reactions. We compare DADM with the other eight state-of-the-art ECG generative models on two real-world databases covering 8 types of drug regimens. The results demonstrate that DADM can more accurately simulate drug-induced changes in ECGs, improving the accuracy by at least 5.79% and recall by 8%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07297v1-abstract-full').style.display = 'none'; document.getElementById('2502.07297v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15262">arXiv:2501.15262</a> <span> [<a href="https://arxiv.org/pdf/2501.15262">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Estimation of Tea Flowering Based on an Improved YOLOv5 and ANN Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Mi%2C+Q">Qianxi Mi</a>, <a href="/search/q-bio?searchtype=author&query=Yuan%2C+P">Pengcheng Yuan</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+C">Chunlei Ma</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiedan Chen</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+M">Mingzhe Yao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15262v1-abstract-short" style="display: inline;"> Tea flowers play a crucial role in taxonomic research and hybrid breeding for the tea plant. Tea flowering consumes the plant's nutrients, and flower thinning can regulate carbon-nitrogen metabolism, enhancing the yield and quality of young shoots. As traditional methods of observing tea flower traits are labor-intensive and inaccurate, we propose an effective framework for tea flowering quantifyi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15262v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15262v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15262v1-abstract-full" style="display: none;"> Tea flowers play a crucial role in taxonomic research and hybrid breeding for the tea plant. Tea flowering consumes the plant's nutrients, and flower thinning can regulate carbon-nitrogen metabolism, enhancing the yield and quality of young shoots. As traditional methods of observing tea flower traits are labor-intensive and inaccurate, we propose an effective framework for tea flowering quantifying. In this study, a highly representative and diverse dataset was constructed by collecting flower images from 29 tea accessions. Based on this dataset, the TflosYOLO model was built on the YOLOv5 architecture and enhanced with the Squeeze-and-Excitation (SE) network, which is the first model to offer a viable solution for detecting tea flowers and predicting flower quantities. The TflosYOLO model achieved an mAP50 of 0.874, outperforming YOLOv5, YOLOv7 and YOLOv8. Furthermore, this model was tested on 34 datasets encompassing 26 tea accessions, five flowering stages, various lighting conditions, and pruned/unpruned plants, demonstrating high generalization and robustness. The correlation coefficient ($ R^2 $) between the predicted and actual flower counts was 0.974. Additionally, the TFSC (Tea Flowering Stage Classification) model - a novel Artificial Neural Network (ANN) was designed for automatic classification of the flowering stages. TFSC achieved an accuracy of 0.899. Dynamic analysis of flowering across 29 tea accessions in 2023 and 2024 was conducted, revealed significant variability in flower quantity and dynamics, with genetically similar accessions showing more consistent flowering patterns. This framework provides a solution for quantifying tea flowering, and can serve as a reference for precision horticulture. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15262v1-abstract-full').style.display = 'none'; document.getElementById('2501.15262v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.06271">arXiv:2501.06271</a> <span> [<a href="https://arxiv.org/pdf/2501.06271">pdf</a>, <a href="https://arxiv.org/format/2501.06271">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> </div> </div> <p class="title is-5 mathjax"> Large Language Models for Bioinformatics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ruan%2C+W">Wei Ruan</a>, <a href="/search/q-bio?searchtype=author&query=Lyu%2C+Y">Yanjun Lyu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+J">Jing Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Cai%2C+J">Jiazhang Cai</a>, <a href="/search/q-bio?searchtype=author&query=Shu%2C+P">Peng Shu</a>, <a href="/search/q-bio?searchtype=author&query=Ge%2C+Y">Yang Ge</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+Y">Yao Lu</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+S">Shang Gao</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yue Wang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+P">Peilong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+L">Lin Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+T">Tao Wang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Y">Yufang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Fang%2C+L">Luyang Fang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Z">Ziyu Liu</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Z">Zhengliang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yiwei Li</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+Z">Zihao Wu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junhao Chen</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+H">Hanqi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Pan%2C+Y">Yi Pan</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Z">Zhenyuan Yang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jingyuan Chen</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+S">Shizhe Liang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+W">Wei Zhang</a> , et al. (30 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.06271v1-abstract-short" style="display: inline;"> With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.06271v1-abstract-full').style.display = 'inline'; document.getElementById('2501.06271v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.06271v1-abstract-full" style="display: none;"> With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.06271v1-abstract-full').style.display = 'none'; document.getElementById('2501.06271v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">64 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.05644">arXiv:2501.05644</a> <span> [<a href="https://arxiv.org/pdf/2501.05644">pdf</a>, <a href="https://arxiv.org/format/2501.05644">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Interpretable Enzyme Function Prediction via Residue-Level Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yang%2C+Z">Zhao Yang</a>, <a href="/search/q-bio?searchtype=author&query=Su%2C+B">Bing Su</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiahao Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wen%2C+J">Ji-Rong Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.05644v1-abstract-short" style="display: inline;"> Predicting multiple functions labeled with Enzyme Commission (EC) numbers from the enzyme sequence is of great significance but remains a challenge due to its sparse multi-label classification nature, i.e., each enzyme is typically associated with only a few labels out of more than 6000 possible EC numbers. However, existing machine learning algorithms generally learn a fixed global representation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.05644v1-abstract-full').style.display = 'inline'; document.getElementById('2501.05644v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.05644v1-abstract-full" style="display: none;"> Predicting multiple functions labeled with Enzyme Commission (EC) numbers from the enzyme sequence is of great significance but remains a challenge due to its sparse multi-label classification nature, i.e., each enzyme is typically associated with only a few labels out of more than 6000 possible EC numbers. However, existing machine learning algorithms generally learn a fixed global representation for each enzyme to classify all functions, thereby they lack interpretability and the fine-grained information of some function-specific local residue fragments may be overwhelmed. Here we present an attention-based framework, namely ProtDETR (Protein Detection Transformer), by casting enzyme function prediction as a detection problem. It uses a set of learnable functional queries to adaptatively extract different local representations from the sequence of residue-level features for predicting different EC numbers. ProtDETR not only significantly outperforms existing deep learning-based enzyme function prediction methods, but also provides a new interpretable perspective on automatically detecting different local regions for identifying different functions through cross-attentions between queries and residue-level features. Code is available at https://github.com/yangzhao1230/ProtDETR. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.05644v1-abstract-full').style.display = 'none'; document.getElementById('2501.05644v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.20014">arXiv:2412.20014</a> <span> [<a href="https://arxiv.org/pdf/2412.20014">pdf</a>, <a href="https://arxiv.org/format/2412.20014">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> ProtCLIP: Function-Informed Protein Multi-Modal Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hanjing Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Yin%2C+M">Mingze Yin</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+W">Wei Wu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Mingyang Li</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+K">Kun Fu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jintai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+J">Jian Wu</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zheng Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.20014v1-abstract-short" style="display: inline;"> Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in various downstream applications. However, these works were still unable to replicate the extraordinary success of language-supervised visual foundation models due to the ineffective usage of aligned protein-text paired data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.20014v1-abstract-full').style.display = 'inline'; document.getElementById('2412.20014v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.20014v1-abstract-full" style="display: none;"> Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in various downstream applications. However, these works were still unable to replicate the extraordinary success of language-supervised visual foundation models due to the ineffective usage of aligned protein-text paired data and the lack of an effective function-informed pre-training paradigm. To address these issues, this paper curates a large-scale protein-text paired dataset called ProtAnno with a property-driven sampling strategy, and introduces a novel function-informed protein pre-training paradigm. Specifically, the sampling strategy determines selecting probability based on the sample confidence and property coverage, balancing the data quality and data quantity in face of large-scale noisy data. Furthermore, motivated by significance of the protein specific functional mechanism, the proposed paradigm explicitly model protein static and dynamic functional segments by two segment-wise pre-training objectives, injecting fine-grained information in a function-informed manner. Leveraging all these innovations, we develop ProtCLIP, a multi-modality foundation model that comprehensively represents function-aware protein embeddings. On 22 different protein benchmarks within 5 types, including protein functionality classification, mutation effect prediction, cross-modal transformation, semantic similarity inference and protein-protein interaction prediction, our ProtCLIP consistently achieves SOTA performance, with remarkable improvements of 75% on average in five cross-modal transformation benchmarks, 59.9% in GO-CC and 39.7% in GO-BP protein function prediction. The experimental results verify the extraordinary potential of ProtCLIP serving as the protein multi-modality foundation model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.20014v1-abstract-full').style.display = 'none'; document.getElementById('2412.20014v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> AAAI 2025 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.18541">arXiv:2412.18541</a> <span> [<a href="https://arxiv.org/pdf/2412.18541">pdf</a>, <a href="https://arxiv.org/format/2412.18541">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> PLD-Tree: Persistent Laplacian Decision Tree for Protein-Protein Binding Free Energy Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xu%2C+X">Xingjian Xu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiahui Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+C">Chunmei Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.18541v1-abstract-short" style="display: inline;"> Recent advances in topology-based modeling have accelerated progress in physical modeling and molecular studies, including applications to protein-ligand binding affinity. In this work, we introduce the Persistent Laplacian Decision Tree (PLD-Tree), a novel method designed to address the challenging task of predicting protein-protein interaction (PPI) affinities. PLD-Tree focuses on protein chains… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.18541v1-abstract-full').style.display = 'inline'; document.getElementById('2412.18541v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.18541v1-abstract-full" style="display: none;"> Recent advances in topology-based modeling have accelerated progress in physical modeling and molecular studies, including applications to protein-ligand binding affinity. In this work, we introduce the Persistent Laplacian Decision Tree (PLD-Tree), a novel method designed to address the challenging task of predicting protein-protein interaction (PPI) affinities. PLD-Tree focuses on protein chains at binding interfaces and employs the persistent Laplacian to capture topological invariants reflecting critical inter-protein interactions. These topological descriptors, derived from persistent homology, are further enhanced by incorporating evolutionary scale modeling (ESM) from a large language model to integrate sequence-based information. We validate PLD-Tree on two benchmark datasets-PDBbind V2020 and SKEMPI v2 demonstrating a correlation coefficient ($R_p$) of 0.83 under the sophisticated leave-out-protein-out cross-validation. Notably, our approach outperforms all reported state-of-the-art methods on these datasets. These results underscore the power of integrating machine learning techniques with topology-based descriptors for molecular docking and virtual screening, providing a robust and accurate framework for predicting protein-protein binding affinities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.18541v1-abstract-full').style.display = 'none'; document.getElementById('2412.18541v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 3 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.06115">arXiv:2412.06115</a> <span> [<a href="https://arxiv.org/pdf/2412.06115">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Protein Evolution as a Complex System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gall%2C+B">Barnabas Gall</a>, <a href="/search/q-bio?searchtype=author&query=Pulsford%2C+S+B">Sacha B. Pulsford</a>, <a href="/search/q-bio?searchtype=author&query=Matthews%2C+D">Dana Matthews</a>, <a href="/search/q-bio?searchtype=author&query=Spence%2C+M+A">Matthew A. Spence</a>, <a href="/search/q-bio?searchtype=author&query=Kaczmarski%2C+J+A">Joe A. Kaczmarski</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J+Z">John Z. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Sandhu%2C+M">Mahakaran Sandhu</a>, <a href="/search/q-bio?searchtype=author&query=Stone%2C+E">Eric Stone</a>, <a href="/search/q-bio?searchtype=author&query=Nichols%2C+J">James Nichols</a>, <a href="/search/q-bio?searchtype=author&query=Jackson%2C+C+J">Colin J. Jackson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.06115v1-abstract-short" style="display: inline;"> Protein evolution underpins life, and understanding its behavior as a system is of great importance. However, our current models of protein evolution are arguably too simplistic to allow quantitative interpretation and prediction of evolutionary trajectories. Viewing protein evolution as a complex system has the potential to advance our understanding and ability to model protein evolution. In this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06115v1-abstract-full').style.display = 'inline'; document.getElementById('2412.06115v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.06115v1-abstract-full" style="display: none;"> Protein evolution underpins life, and understanding its behavior as a system is of great importance. However, our current models of protein evolution are arguably too simplistic to allow quantitative interpretation and prediction of evolutionary trajectories. Viewing protein evolution as a complex system has the potential to advance our understanding and ability to model protein evolution. In this perspective, we discuss aspects of protein evolution that are typical of complex systems, from nonlinear dynamics, sensitivity to initial conditions, self-organization, and the emergence of order from chaos and disorder. We discuss how the growth in sequence and structural data, insights from laboratory evolution and new machine learning tools can advance the study of protein evolution and that by treating protein evolution as a complex adaptive system, we may gain new insights into the fundamental principles driving biological innovation and adaptation and apply this to protein engineering and design. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06115v1-abstract-full').style.display = 'none'; document.getElementById('2412.06115v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.15684">arXiv:2411.15684</a> <span> [<a href="https://arxiv.org/pdf/2411.15684">pdf</a>, <a href="https://arxiv.org/ps/2411.15684">ps</a>, <a href="https://arxiv.org/format/2411.15684">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zheng Ma</a>, <a href="/search/q-bio?searchtype=author&query=Mao%2C+Z">Zeping Mao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+R">Ruixue Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiazhen Chen</a>, <a href="/search/q-bio?searchtype=author&query=Xin%2C+L">Lei Xin</a>, <a href="/search/q-bio?searchtype=author&query=Shan%2C+P">Paul Shan</a>, <a href="/search/q-bio?searchtype=author&query=Ghodsi%2C+A">Ali Ghodsi</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Ming Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.15684v1-abstract-short" style="display: inline;"> Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a ne… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15684v1-abstract-full').style.display = 'inline'; document.getElementById('2411.15684v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.15684v1-abstract-full" style="display: none;"> Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a new deep learning method DIANovo, and address each of these difficulties, and improves the previous established system DeepNovo-DIA by from 25% to 81%, averaging 48%, for amino acid recall, and by from 27% to 89%, averaging 57%, for peptide recall, by equipping the model with a deeper understanding of coeluted DIA spectra. This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode. We find that while DIA excels with narrow isolation windows on older-generation instruments, it loses its advantage with wider windows. However, with Orbitrap Astral, DIA consistently outperforms DDA due to narrow window mode enabled. We also provide a theoretical explanation of this phenomenon, emphasizing the critical role of the signal-to-noise profile in the successful application of de novo sequencing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15684v1-abstract-full').style.display = 'none'; document.getElementById('2411.15684v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.15215">arXiv:2411.15215</a> <span> [<a href="https://arxiv.org/pdf/2411.15215">pdf</a>, <a href="https://arxiv.org/format/2411.15215">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> S$^2$ALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yin%2C+M">Mingze Yin</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hanjing Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+J">Jialu Wu</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Y">Yiheng Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Zhan%2C+Y">Yuxuan Zhan</a>, <a href="/search/q-bio?searchtype=author&query=Kong%2C+Z">Zitai Kong</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+H">Hongxia Xu</a>, <a href="/search/q-bio?searchtype=author&query=Hsieh%2C+C">Chang-Yu Hsieh</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jintai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Hou%2C+T">Tingjun Hou</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+J">Jian Wu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.15215v1-abstract-short" style="display: inline;"> Antibodies safeguard our health through their precise and potent binding to specific antigens, demonstrating promising therapeutic efficacy in the treatment of numerous diseases, including COVID-19. Recent advancements in biomedical language models have shown the great potential to interpret complex biological structures and functions. However, existing antibody specific models have a notable limi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15215v1-abstract-full').style.display = 'inline'; document.getElementById('2411.15215v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.15215v1-abstract-full" style="display: none;"> Antibodies safeguard our health through their precise and potent binding to specific antigens, demonstrating promising therapeutic efficacy in the treatment of numerous diseases, including COVID-19. Recent advancements in biomedical language models have shown the great potential to interpret complex biological structures and functions. However, existing antibody specific models have a notable limitation that they lack explicit consideration for antibody structural information, despite the fact that both 1D sequence and 3D structure carry unique and complementary insights into antibody behavior and functionality. This paper proposes Sequence-Structure multi-level pre-trained Antibody Language Model (S$^2$ALM), combining holistic sequential and structural information in one unified, generic antibody foundation model. We construct a hierarchical pre-training paradigm incorporated with two customized multi-level training objectives to facilitate the modeling of comprehensive antibody representations. S$^2$ALM's representation space uncovers inherent functional binding mechanisms, biological evolution properties and structural interaction patterns. Pre-trained over 75 million sequences and 11.7 million structures, S$^2$ALM can be adopted for diverse downstream tasks: accurately predicting antigen-antibody binding affinities, precisely distinguishing B cell maturation stages, identifying antibody crucial binding positions, and specifically designing novel coronavirus-binding antibodies. Remarkably, S$^2$ALM outperforms well-established and renowned baselines and sets new state-of-the-art performance across extensive antibody specific understanding and generation tasks. S$^2$ALM's ability to model comprehensive and generalized representations further positions its potential to advance real-world therapeutic antibody development, potentially addressing unmet academic, industrial, and clinical needs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15215v1-abstract-full').style.display = 'none'; document.getElementById('2411.15215v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12957">arXiv:2411.12957</a> <span> [<a href="https://arxiv.org/pdf/2411.12957">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sandhu%2C+M">Mahakaran Sandhu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">John Chen</a>, <a href="/search/q-bio?searchtype=author&query=Matthews%2C+D">Dana Matthews</a>, <a href="/search/q-bio?searchtype=author&query=Spence%2C+M+A">Matthew A Spence</a>, <a href="/search/q-bio?searchtype=author&query=Pulsford%2C+S+B">Sacha B Pulsford</a>, <a href="/search/q-bio?searchtype=author&query=Gall%2C+B">Barnabas Gall</a>, <a href="/search/q-bio?searchtype=author&query=Nichols%2C+J">James Nichols</a>, <a href="/search/q-bio?searchtype=author&query=Tokuriki%2C+N">Nobuhiko Tokuriki</a>, <a href="/search/q-bio?searchtype=author&query=Jackson%2C+C+J">Colin J Jackson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12957v1-abstract-short" style="display: inline;"> Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many proteins, especially those with complex fun… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12957v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12957v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12957v1-abstract-full" style="display: none;"> Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many proteins, especially those with complex functions or under multiple selection pressures, exist on rugged fitness landscapes. Here we discuss the theoretical framework that underpins our understanding of fitness landscapes, alongside recent work that has advanced our understanding - particularly the biophysical basis for smoothness versus ruggedness. Finally, we address the rapid advances that have been made in computational and experimental exploration and exploitation of fitness landscapes, and how these can identify efficient routes to protein optimization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12957v1-abstract-full').style.display = 'none'; document.getElementById('2411.12957v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 4 figures, submitted to Biochemistry</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07503">arXiv:2411.07503</a> <span> [<a href="https://arxiv.org/pdf/2411.07503">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+S">Shengqi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zilin Wang</a>, <a href="/search/q-bio?searchtype=author&query=Dai%2C+J">Jianrong Dai</a>, <a href="/search/q-bio?searchtype=author&query=Qin%2C+S">Shirui Qin</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+Y">Ying Cao</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+R">Ruiao Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiayun Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+G">Guohua Wu</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+Y">Yuan Tang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07503v2-abstract-short" style="display: inline;"> Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation. Materials and Methods: We developed a novel MRIgRT mot… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07503v2-abstract-full').style.display = 'inline'; document.getElementById('2411.07503v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07503v2-abstract-full" style="display: none;"> Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation. Materials and Methods: We developed a novel MRIgRT motion tracking and segmentation method by integrating the ETLD framework with an improved Chan-Vese model (ICV), named ETLD+ICV. The ETLD framework was upgraded for real-time cine MRI, including advanced image preprocessing, no-reference image quality assessment, an enhanced median-flow tracker, and a refined detector with dynamic search region adjustments. ICV was used for precise target volume coverage, refining the segmented region frame by frame using tracking results, with key parameters optimized. The method was tested on 3.5D MRI scans from 10 patients with liver metastases. Results: Evaluation of 106,000 frames across 77 treatment fractions showed sub-millimeter tracking errors of less than 0.8mm, with over 99% precision and 98% recall for all subjects in the Beam Eye View(BEV)/Beam Path View(BPV) orientation. The ETLD+ICV method achieved a dice global score of more than 82% for all subjects, demonstrating the method's extensibility and precise target volume coverage. Conclusion: This study successfully developed an automatic real-time markerless motion tracking method for MRIgRT that significantly outperforms current methods. The novel method not only delivers exceptional precision in tracking and segmentation but also shows enhanced adaptability to clinical demands, making it an indispensable asset in improving the efficacy of radiotherapy treatments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07503v2-abstract-full').style.display = 'none'; document.getElementById('2411.07503v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.03111">arXiv:2411.03111</a> <span> [<a href="https://arxiv.org/pdf/2411.03111">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Radical-mediated Electrical Enzyme Assay For At-home Clinical Test </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jang%2C+H">Hyun-June Jang</a>, <a href="/search/q-bio?searchtype=author&query=Joung%2C+H">Hyou-Arm Joung</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+X">Xiaoao Shi</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+R">Rui Ding</a>, <a href="/search/q-bio?searchtype=author&query=Wagner%2C+J">Justine Wagner</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+E">Erting Tang</a>, <a href="/search/q-bio?searchtype=author&query=Zhuang%2C+W">Wen Zhuang</a>, <a href="/search/q-bio?searchtype=author&query=Ryu%2C+B">Byunghoon Ryu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Guanmin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Yeo%2C+K+J">Kiang-Teck Jerry Yeo</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jun Huang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junhong Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.03111v1-abstract-short" style="display: inline;"> To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device. The REEA utilizes horseradish peroxidase (HRP) to catalyze the conversion of aromatic substrates into radical forms, producing protons detecte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03111v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03111v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03111v1-abstract-full" style="display: none;"> To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device. The REEA utilizes horseradish peroxidase (HRP) to catalyze the conversion of aromatic substrates into radical forms, producing protons detected by an ion-sensitive FET for biomarker quantification. Through screening 14 phenolic compounds, halogenated phenols emerged as optimal substrates for the REEA. Encased in an affordable cartridge ($0.55 per test), the system achieved a detection limit of 146 fg/mL for estradiol (E2), with a coefficient of variation (CV) below 9.2% in E2-spiked samples and an r2 of 0.963 across a measuring range of 19 to 4,551 pg/mL in clinical plasma samples, providing results in under 10 minutes. This adaptable system not only promises to offer a fast and reliable platform, but also holds significant potential for expansion to a wide array of biomarkers, paving the way for broader clinical and home-based applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03111v1-abstract-full').style.display = 'none'; document.getElementById('2411.03111v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.04223">arXiv:2410.04223</a> <span> [<a href="https://arxiv.org/pdf/2410.04223">pdf</a>, <a href="https://arxiv.org/format/2410.04223">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liu%2C+G">Gang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+M">Michael Sun</a>, <a href="/search/q-bio?searchtype=author&query=Matusik%2C+W">Wojciech Matusik</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+M">Meng Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jie Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.04223v1-abstract-short" style="display: inline;"> While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inver… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04223v1-abstract-full').style.display = 'inline'; document.getElementById('2410.04223v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.04223v1-abstract-full" style="display: none;"> While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inverse design with retrosynthetic planning. Llamole integrates a base LLM with the Graph Diffusion Transformer and Graph Neural Networks for multi-conditional molecular generation and reaction inference within texts, while the LLM, with enhanced molecular understanding, flexibly controls activation among the different graph modules. Additionally, Llamole integrates A* search with LLM-based cost functions for efficient retrosynthetic planning. We create benchmarking datasets and conduct extensive experiments to evaluate Llamole against in-context learning and supervised fine-tuning. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04223v1-abstract-full').style.display = 'none'; document.getElementById('2410.04223v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages, 11 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03328">arXiv:2410.03328</a> <span> [<a href="https://arxiv.org/pdf/2410.03328">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Double-Strand Break Clustering: An Economical and Effective Strategy for DNA Repair </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junyi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wenzong Ma</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Y">Yuqi Ma</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+G">Gen Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03328v1-abstract-short" style="display: inline;"> In mammalian cells, repair centers for DNA double-strand breaks (DSBs) have been identified. However, previous researches predominantly rely on methods that induce specific DSBs by cutting particular DNA sequences. The clustering and its spatiotemporal properties of non-specifically DSBs, especially those induced by environmental stresses such as irradiation, remains unclear. In this study, we use… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03328v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03328v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03328v1-abstract-full" style="display: none;"> In mammalian cells, repair centers for DNA double-strand breaks (DSBs) have been identified. However, previous researches predominantly rely on methods that induce specific DSBs by cutting particular DNA sequences. The clustering and its spatiotemporal properties of non-specifically DSBs, especially those induced by environmental stresses such as irradiation, remains unclear. In this study, we used Dragonfly microscopy to induce high-precision damage in cells and discovered that DSB clustering during the early stages of DNA damage response (DDR) and repair, but not during the repair plateau phase. Early in DDR, DSB clustered into existing 53BP1 foci. The DSB clustering at different stages has different implications for DNA repair. By controlling the distance between adjacent damage points, we found that the probability of DSB clustering remains constant at distances of 0.8 - 1.4 um, while clustering does not occur beyond 1.4 um. Within the 0.8 um range, the probability of clustering significantly increases due to the phase separation effect of 53BP1. Using a Monte Carlo approach, we developed a dynamic model of 53BP1 foci formation, fission, and fusion. This model accurately predicts experimental outcomes and further demonstrates the temporal and spatial influences on DSB clustering. These results showed that, similarly to specifically induced DSBs, non-specifically induced DSBs can also cluster. The extent of DSB clustering is influenced by both temporal and spatial factors, which provide new insights into the dynamics of DSB clustering and the role of 53BP1 in DNA repair processes. Such findings could enhance our understanding of DNA damage responses and help us improve DNA repair therapies in disease. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03328v1-abstract-full').style.display = 'none'; document.getElementById('2410.03328v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.14617">arXiv:2409.14617</a> <span> [<a href="https://arxiv.org/pdf/2409.14617">pdf</a>, <a href="https://arxiv.org/format/2409.14617">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Protein-Mamba: Biological Mamba Models for Protein Function Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xu%2C+B">Bohao Xu</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+Y">Yingzhou Lu</a>, <a href="/search/q-bio?searchtype=author&query=Inoue%2C+Y">Yoshitaka Inoue</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+N">Namkyeong Lee</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+T">Tianfan Fu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jintai Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.14617v1-abstract-short" style="display: inline;"> Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models often struggle with the complexity and variability inherent in predicting protein functions, necessitating more sophisticated approaches. In this work, we introduce Protein-Mamba, a novel two-stage model that leverages both… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14617v1-abstract-full').style.display = 'inline'; document.getElementById('2409.14617v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.14617v1-abstract-full" style="display: none;"> Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models often struggle with the complexity and variability inherent in predicting protein functions, necessitating more sophisticated approaches. In this work, we introduce Protein-Mamba, a novel two-stage model that leverages both self-supervised learning and fine-tuning to improve protein function prediction. The pre-training stage allows the model to capture general chemical structures and relationships from large, unlabeled datasets, while the fine-tuning stage refines these insights using specific labeled datasets, resulting in superior prediction performance. Our extensive experiments demonstrate that Protein-Mamba achieves competitive performance, compared with a couple of state-of-the-art methods across a range of protein function datasets. This model's ability to effectively utilize both unlabeled and labeled data highlights the potential of self-supervised learning in advancing protein function prediction and offers a promising direction for future research in drug discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14617v1-abstract-full').style.display = 'none'; document.getElementById('2409.14617v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.09183">arXiv:2409.09183</a> <span> [<a href="https://arxiv.org/pdf/2409.09183">pdf</a>, <a href="https://arxiv.org/ps/2409.09183">ps</a>, <a href="https://arxiv.org/format/2409.09183">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Quantum-inspired Reinforcement Learning for Synthesizable Drug Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+D">Dannong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jintai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+Z">Zhiding Liang</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+T">Tianfan Fu</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+X">Xiao-Yang Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.09183v1-abstract-short" style="display: inline;"> Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-relevant oracle functions (i.e., objective) while ensuring synthetic feasibility. However, existing methods are mostly based on random search. To address this issue, in this pa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09183v1-abstract-full').style.display = 'inline'; document.getElementById('2409.09183v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.09183v1-abstract-full" style="display: none;"> Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-relevant oracle functions (i.e., objective) while ensuring synthetic feasibility. However, existing methods are mostly based on random search. To address this issue, in this paper, we introduce a novel approach using the reinforcement learning method with quantum-inspired simulated annealing policy neural network to navigate the vast discrete space of chemical structures intelligently. Specifically, we employ a deterministic REINFORCE algorithm using policy neural networks to output transitional probability to guide state transitions and local search using genetic algorithm to refine solutions to a local optimum within each iteration. Our methods are evaluated with the Practical Molecular Optimization (PMO) benchmark framework with a 10K query budget. We further showcase the competitive performance of our method by comparing it against the state-of-the-art genetic algorithms-based method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09183v1-abstract-full').style.display = 'none'; document.getElementById('2409.09183v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05873">arXiv:2409.05873</a> <span> [<a href="https://arxiv.org/pdf/2409.05873">pdf</a>, <a href="https://arxiv.org/format/2409.05873">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Syntax-Guided Procedural Synthesis of Molecules </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+M">Michael Sun</a>, <a href="/search/q-bio?searchtype=author&query=Lo%2C+A">Alston Lo</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+W">Wenhao Gao</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+M">Minghao Guo</a>, <a href="/search/q-bio?searchtype=author&query=Thost%2C+V">Veronika Thost</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jie Chen</a>, <a href="/search/q-bio?searchtype=author&query=Coley%2C+C">Connor Coley</a>, <a href="/search/q-bio?searchtype=author&query=Matusik%2C+W">Wojciech Matusik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.05873v1-abstract-short" style="display: inline;"> Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05873v1-abstract-full').style.display = 'inline'; document.getElementById('2409.05873v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05873v1-abstract-full" style="display: none;"> Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for reasoning about the combinatorial space of synthesis pathways. Given a molecule we aim to generate analogs for, we iteratively refine its skeletal characteristics via Markov Chain Monte Carlo simulations over the space of syntactic skeletons. Given a black-box oracle to optimize, we formulate a joint design space over syntactic templates and molecular descriptors and introduce evolutionary algorithms that optimize both syntactic and semantic dimensions synergistically. Our key insight is that once the syntactic skeleton is set, we can amortize over the search complexity of deriving the program's semantics by training policies to fully utilize the fixed horizon Markov Decision Process imposed by the syntactic template. We demonstrate performance advantages of our bilevel framework for synthesizable analog generation and synthesizable molecule design. Notably, our approach offers the user explicit control over the resources required to perform synthesis and biases the design space towards simpler solutions, making it particularly promising for autonomous synthesis platforms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05873v1-abstract-full').style.display = 'none'; document.getElementById('2409.05873v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.13479">arXiv:2408.13479</a> <span> [<a href="https://arxiv.org/pdf/2408.13479">pdf</a>, <a href="https://arxiv.org/format/2408.13479">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Quantum-machine-assisted Drug Discovery: Survey and Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhou%2C+Y">Yidong Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jintai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+J">Jinglei Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Karemore%2C+G">Gopal Karemore</a>, <a href="/search/q-bio?searchtype=author&query=Zitnik%2C+M">Marinka Zitnik</a>, <a href="/search/q-bio?searchtype=author&query=Chong%2C+F+T">Frederic T. Chong</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Junyu Liu</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+T">Tianfan Fu</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+Z">Zhiding Liang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.13479v3-abstract-short" style="display: inline;"> Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market. Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process, but the development of quantum computing offers potential due to its unique capabilities. This paper discusses the integ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13479v3-abstract-full').style.display = 'inline'; document.getElementById('2408.13479v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.13479v3-abstract-full" style="display: none;"> Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market. Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process, but the development of quantum computing offers potential due to its unique capabilities. This paper discusses the integration of quantum computing into drug discovery and development, focusing on how quantum technologies might accelerate and enhance various stages of the drug development cycle. Specifically, we explore the application of quantum computing in addressing challenges related to drug discovery, such as molecular simulation and the prediction of drug-target interactions, as well as the optimization of clinical trial outcomes. By leveraging the inherent capabilities of quantum computing, we might be able to reduce the time and cost associated with bringing new drugs to market, ultimately benefiting public health. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13479v3-abstract-full').style.display = 'none'; document.getElementById('2408.13479v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages, 10 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.09142">arXiv:2408.09142</a> <span> [<a href="https://arxiv.org/pdf/2408.09142">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> </div> <p class="title is-5 mathjax"> NP-TCMtarget: a network pharmacology platform for exploring mechanisms of action of Traditional Chinese medicine </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+A">Aoyi Wang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yingdong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+H">Haoyang Peng</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+H">Haoran Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+C">Caiping Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+J">Jinzhong Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+W">Wuxia Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jianxin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+P">Peng Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.09142v1-abstract-short" style="display: inline;"> The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput drug-target interaction predicting models and biological profile-base… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09142v1-abstract-full').style.display = 'inline'; document.getElementById('2408.09142v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.09142v1-abstract-full" style="display: none;"> The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput drug-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between drug/disease inducible gene expression profiles and specific gene signatures for 8,233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal components-direct targets-indirect targets by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09142v1-abstract-full').style.display = 'none'; document.getElementById('2408.09142v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.05696">arXiv:2408.05696</a> <span> [<a href="https://arxiv.org/pdf/2408.05696">pdf</a>, <a href="https://arxiv.org/format/2408.05696">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xu%2C+B">Bohao Xu</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+Y">Yingzhou Lu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+C">Chenhao Li</a>, <a href="/search/q-bio?searchtype=author&query=Yue%2C+L">Ling Yue</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+X">Xiao Wang</a>, <a href="/search/q-bio?searchtype=author&query=Hao%2C+N">Nan Hao</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+T">Tianfan Fu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jim Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.05696v1-abstract-short" style="display: inline;"> In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy. However, the process of accurately predicting these properties is often resource-intensive and requires extensive experimental data. To address this challenge, we propose SMILES-Mamba, a two-stage model that leverag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05696v1-abstract-full').style.display = 'inline'; document.getElementById('2408.05696v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.05696v1-abstract-full" style="display: none;"> In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy. However, the process of accurately predicting these properties is often resource-intensive and requires extensive experimental data. To address this challenge, we propose SMILES-Mamba, a two-stage model that leverages both unlabeled and labeled data through a combination of self-supervised pretraining and fine-tuning strategies. The model first pre-trains on a large corpus of unlabeled SMILES strings to capture the underlying chemical structure and relationships, before being fine-tuned on smaller, labeled datasets specific to ADMET tasks. Our results demonstrate that SMILES-Mamba exhibits competitive performance across 22 ADMET datasets, achieving the highest score in 14 tasks, highlighting the potential of self-supervised learning in improving molecular property prediction. This approach not only enhances prediction accuracy but also reduces the dependence on large, labeled datasets, offering a promising direction for future research in drug discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05696v1-abstract-full').style.display = 'none'; document.getElementById('2408.05696v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.05695">arXiv:2408.05695</a> <span> [<a href="https://arxiv.org/pdf/2408.05695">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> </div> </div> <p class="title is-5 mathjax"> Advancements in Programmable Lipid Nanoparticles: Exploring the Four-Domain Model for Targeted Drug Delivery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liu%2C+Z">Zhaoyu Liu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jingxun Chen</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+M">Mingkun Xu</a>, <a href="/search/q-bio?searchtype=author&query=Gracias%2C+D+H">David H. Gracias</a>, <a href="/search/q-bio?searchtype=author&query=Yong%2C+K">Ken-Tye Yong</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+Y">Yuanyuan Wei</a>, <a href="/search/q-bio?searchtype=author&query=Ho%2C+H">Ho-Pui Ho</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.05695v3-abstract-short" style="display: inline;"> Programmable lipid nanoparticles, or LNPs, represent a breakthrough in the realm of targeted drug delivery, offering precise spatiotemporal control essential for the treatment of complex diseases such as cancer and genetic disorders. In order to provide a more modular perspective and a more balanced analysis of the mechanism, this review presents a novel Four-Domain Model that consists of Architec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05695v3-abstract-full').style.display = 'inline'; document.getElementById('2408.05695v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.05695v3-abstract-full" style="display: none;"> Programmable lipid nanoparticles, or LNPs, represent a breakthrough in the realm of targeted drug delivery, offering precise spatiotemporal control essential for the treatment of complex diseases such as cancer and genetic disorders. In order to provide a more modular perspective and a more balanced analysis of the mechanism, this review presents a novel Four-Domain Model that consists of Architecture, Interface, Payload, and Dispersal Domain. We explored the dynamical equilibrium between LNPs components and the surroundings throughout their destiny, from formulation to release. On the basis of this, we delve deep into manufacturing challenges, scalability issues, and regulatory hurdles, associated with the clinical translation of LNP technology. Within the framework focusing on the programmability in each domain, we prioritized patient-centric factors like dosing regimens, administration techniques, and potential consequences. Notably, this review expands to innovative anatomical routes, such as intranasal and intraocular administration, offering a thorough examination of the advantages and disadvantages of each route. We also offered a comprehensive comparison between artificial LNPs and natural exosomes in terms of functionality, biocompatibility, and therapeutic potential. Ultimately, this review highlights the potential of programmable LNPs to evolve into more intelligent, naturally integrated systems, achieving optimal biocompatibility and functionality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05695v3-abstract-full').style.display = 'none'; document.getElementById('2408.05695v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">46 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.10411">arXiv:2407.10411</a> <span> [<a href="https://arxiv.org/pdf/2407.10411">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.23977/tracam.2024.040107">10.23977/tracam.2024.040107 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Study on Lampreys Population Based on Sex-Ratio-Related Growth-Balance Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ji%2C+Z">Zuhua Ji</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiarui Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zihang Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.10411v1-abstract-short" style="display: inline;"> Lampreys are one of the oldest species in the world, living longer than dinosaurs, which is related to the ability to change the sex ratio during their lifespan. In this paper, to understand how sex ratio and food quantity affect the population growth rate of lampreys, the researchers draw inspiration from the logistics model and established a model called EcoSexChange(ESC), which results in a pop… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10411v1-abstract-full').style.display = 'inline'; document.getElementById('2407.10411v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.10411v1-abstract-full" style="display: none;"> Lampreys are one of the oldest species in the world, living longer than dinosaurs, which is related to the ability to change the sex ratio during their lifespan. In this paper, to understand how sex ratio and food quantity affect the population growth rate of lampreys, the researchers draw inspiration from the logistics model and established a model called EcoSexChange(ESC), which results in a population initially increasing and then stabilizing, a reasonable outcome that may apply to other organisms with significant differences in consumption between sexes. Subsequently, this paper develops the Sex Ratio Adaptation Eco Impact (SRAEI) model based on the ESC model using the ABM algorithm to simulate how the population of lampreys, whose lives are divided into seven stages, grows and stabilizes. Then introduces a sudden disaster factor in the middle of the simulation, while also comparing lampreys that cannot adjust their sex ratio. The results of this paper are of great reference significance for people to analyze the population changes of lampreys in different living environments, and they are also easy to apply to other species with large differences between males and females. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10411v1-abstract-full').style.display = 'none'; document.getElementById('2407.10411v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Transactions on Computational and Applied Mathematics. 2024 May 6;4(1):48-55 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.04799">arXiv:2407.04799</a> <span> [<a href="https://arxiv.org/pdf/2407.04799">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> A state-space catch-at-length assessment model for redfish on the Eastern Grand Bank of Newfoundland reveals large uncertainties in data and stock dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Cadigan%2C+N+G">Noel G. Cadigan</a>, <a href="/search/q-bio?searchtype=author&query=Perreault%2C+A+M">Andrea M. Perreault</a>, <a href="/search/q-bio?searchtype=author&query=Nguyen%2C+H">Hoang Nguyen</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiaying Chen</a>, <a href="/search/q-bio?searchtype=author&query=Beita-Jimenez%2C+A">Andres Beita-Jimenez</a>, <a href="/search/q-bio?searchtype=author&query=Fuller%2C+N">Natalie Fuller</a>, <a href="/search/q-bio?searchtype=author&query=Ransier%2C+K">Krista Ransier</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.04799v1-abstract-short" style="display: inline;"> We developed a state-space age-structured catch-at-length (ACL) assessment model for redfish in NAFO Divisions 3LN. The model was developed to address limitations in the surplus production model that was previously used to assess this stock. The ACL model included temporal variations in recruitment, growth, and mortality rates, which were limitations identified for the surplus production model. Ou… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04799v1-abstract-full').style.display = 'inline'; document.getElementById('2407.04799v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.04799v1-abstract-full" style="display: none;"> We developed a state-space age-structured catch-at-length (ACL) assessment model for redfish in NAFO Divisions 3LN. The model was developed to address limitations in the surplus production model that was previously used to assess this stock. The ACL model included temporal variations in recruitment, growth, and mortality rates, which were limitations identified for the surplus production model. Our ACL model revealed some important discrepancies in survey and fishery length compositions. Our model also required large population dynamics process errors to achieve good fits to survey indices and catch estimates, which also demonstrated that additional understanding of these data and other model assumptions is required. As such, we do not propose the ACL model to provide management advice for 3LN redfish, but we do provide research recommendations that should provide a better basis to model the 3LN redfish stock dynamics. Recommendations include implementing sampling programs to determine redfish species/ecotypes in commercial and research survey catches and improving biological sampling for maturity and age. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04799v1-abstract-full').style.display = 'none'; document.getElementById('2407.04799v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages including references, tables, and figures. In addition 12 pages figures in an Appendix</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.18531">arXiv:2406.18531</a> <span> [<a href="https://arxiv.org/pdf/2406.18531">pdf</a>, <a href="https://arxiv.org/format/2406.18531">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> A principled framework to assess the information-theoretic fitness of brain functional sub-circuits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Duong-Tran%2C+D">Duy Duong-Tran</a>, <a href="/search/q-bio?searchtype=author&query=Nguyen%2C+N">Nghi Nguyen</a>, <a href="/search/q-bio?searchtype=author&query=Mu%2C+S">Shizhuo Mu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiong Chen</a>, <a href="/search/q-bio?searchtype=author&query=Bao%2C+J">Jingxuan Bao</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+F">Frederick Xu</a>, <a href="/search/q-bio?searchtype=author&query=Garai%2C+S">Sumita Garai</a>, <a href="/search/q-bio?searchtype=author&query=Cadena-Pico%2C+J">Jose Cadena-Pico</a>, <a href="/search/q-bio?searchtype=author&query=Kaplan%2C+A+D">Alan David Kaplan</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+T">Tianlong Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Yize Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+L">Li Shen</a>, <a href="/search/q-bio?searchtype=author&query=Go%C3%B1i%2C+J">Joaqu铆n Go帽i</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.18531v2-abstract-short" style="display: inline;"> In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18531v2-abstract-full').style.display = 'inline'; document.getElementById('2406.18531v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.18531v2-abstract-full" style="display: none;"> In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18531v2-abstract-full').style.display = 'none'; document.getElementById('2406.18531v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.13292">arXiv:2406.13292</a> <span> [<a href="https://arxiv.org/pdf/2406.13292">pdf</a>, <a href="https://arxiv.org/format/2406.13292">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Dolci%2C+G">Giorgio Dolci</a>, <a href="/search/q-bio?searchtype=author&query=Cruciani%2C+F">Federica Cruciani</a>, <a href="/search/q-bio?searchtype=author&query=Rahaman%2C+M+A">Md Abdur Rahaman</a>, <a href="/search/q-bio?searchtype=author&query=Abrol%2C+A">Anees Abrol</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiayu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+Z">Zening Fu</a>, <a href="/search/q-bio?searchtype=author&query=Galazzo%2C+I+B">Ilaria Boscolo Galazzo</a>, <a href="/search/q-bio?searchtype=author&query=Menegaz%2C+G">Gloria Menegaz</a>, <a href="/search/q-bio?searchtype=author&query=Calhoun%2C+V+D">Vince D. Calhoun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.13292v3-abstract-short" style="display: inline;"> \textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. The objective of the work was to capture structural and functional modulations of brain structure and function relying on multimodal MRI data and Single Nucleotide Polymorph… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13292v3-abstract-full').style.display = 'inline'; document.getElementById('2406.13292v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.13292v3-abstract-full" style="display: none;"> \textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. The objective of the work was to capture structural and functional modulations of brain structure and function relying on multimodal MRI data and Single Nucleotide Polymorphisms, also in case of missing views, with the twofold goal of classifying AD patients versus healthy controls and detecting MCI converters. % in two distinct tasks, dealing with also missing data.\\ \textbf{Approach:} We propose a multimodal DL-based classification framework where a generative module employing Cycle Generative Adversarial Networks was introduced in the latent space for imputing missing data (a common issue of multimodal approaches). Explainable AI method was then used to extract input features' relevance allowing for post-hoc validation and enhancing the interpretability of the learned representations. \textbf{Main results:} Experimental results on two tasks, AD detection and MCI conversion, showed that our framework reached competitive performance in the state-of-the-art with an accuracy of $0.926\pm0.02$ and $0.711\pm0.01$ in the two tasks, respectively. The interpretability analysis revealed gray matter modulations in cortical and subcortical brain areas typically associated with AD. Moreover, impairments in sensory-motor and visual resting state networks along the disease continuum, as well as genetic mutations defining biological processes linked to endocytosis, amyloid-beta, and cholesterol, were identified. \textbf{Significance:} Our integrative and interpretable DL approach shows promising performance for AD detection and MCI prediction while shedding light on important biological insights. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13292v3-abstract-full').style.display = 'none'; document.getElementById('2406.13292v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 8 figures (main text + supplementary materials), submitted to a journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.13284">arXiv:2406.13284</a> <span> [<a href="https://arxiv.org/pdf/2406.13284">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> The association of domain-specific physical activity and sedentary activity with stroke: A prospective cohort study </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=He%2C+X">Xinyi He</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+S">Shidi Wang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yi Li</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Jiucun Wang</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+G">Guangrui Yang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jun Chen</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+Z">Zixin Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.13284v1-abstract-short" style="display: inline;"> Background The incidence of stroke places a heavy burden on both society and individuals. Activity is closely related to cardiovascular health. This study aimed to investigate the relationship between the varying domains of PA, like occupation-related Physical Activity (OPA), transportation-related Physical Activity (TPA), leisure-time Physical Activity (LTPA), and Sedentary Activity (SA) with str… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13284v1-abstract-full').style.display = 'inline'; document.getElementById('2406.13284v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.13284v1-abstract-full" style="display: none;"> Background The incidence of stroke places a heavy burden on both society and individuals. Activity is closely related to cardiovascular health. This study aimed to investigate the relationship between the varying domains of PA, like occupation-related Physical Activity (OPA), transportation-related Physical Activity (TPA), leisure-time Physical Activity (LTPA), and Sedentary Activity (SA) with stroke. Methods Our analysis included 30,400 participants aged 20+ years from 2007 to 2018 National Health and Nutrition Examination Survey (NHANES). Stroke was identified based on the participant's self-reported diagnoses from previous medical consultations, and PA and SA were self-reported. Multivariable logistic and restricted cubic spline models were used to assess the associations. Results Participants achieving PA guidelines (performing PA more than 150 min/week) were 35.7% less likely to have a stroke based on both the total PA (odds ratio [OR] 0.643, 95% confidence interval [CI] 0.523-0.790) and LTPA (OR 0.643, 95% CI 0.514-0.805), while OPA or TPA did not demonstrate lower stroke risk. Furthermore, participants with less than 7.5 h/day SA levels were 21.6% (OR 0.784, 95% CI 0.665-0.925) less likely to have a stroke. The intensities of total PA and LTPA exhibited nonlinear U-shaped associations with stroke risk. In contrast, those of OPA and TPA showed negative linear associations, while SA intensities were positively linearly correlated with stroke risk. Conclusions LTPA, but not OPA or TPA, was associated with a lower risk of stroke at any amount, suggesting that significant cardiovascular health would benefit from increased PA. Additionally, the positive association between SA and stroke indicated that prolonged sitting was detrimental to cardiovascular health. Overall, increased PA within a reasonable range reduces the risk of stroke, while increased SA elevates it. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13284v1-abstract-full').style.display = 'none'; document.getElementById('2406.13284v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.06393">arXiv:2406.06393</a> <span> [<a href="https://arxiv.org/pdf/2406.06393">pdf</a>, <a href="https://arxiv.org/format/2406.06393">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiawen Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+M">Muqing Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+W">Wenrong Wu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+J">Jinwei Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yun Li</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+D">Didong Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.06393v2-abstract-short" style="display: inline;"> Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology ima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.06393v2-abstract-full').style.display = 'inline'; document.getElementById('2406.06393v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.06393v2-abstract-full" style="display: none;"> Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000-30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.06393v2-abstract-full').style.display = 'none'; document.getElementById('2406.06393v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.4.10; I.2.10 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.15805">arXiv:2405.15805</a> <span> [<a href="https://arxiv.org/pdf/2405.15805">pdf</a>, <a href="https://arxiv.org/format/2405.15805">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.media.2025.103462">10.1016/j.media.2025.103462 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Thapaliya%2C+B">Bishal Thapaliya</a>, <a href="/search/q-bio?searchtype=author&query=Miller%2C+R">Robyn Miller</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiayu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yu-Ping Wang</a>, <a href="/search/q-bio?searchtype=author&query=Akbas%2C+E">Esra Akbas</a>, <a href="/search/q-bio?searchtype=author&query=Sapkota%2C+R">Ram Sapkota</a>, <a href="/search/q-bio?searchtype=author&query=Ray%2C+B">Bhaskar Ray</a>, <a href="/search/q-bio?searchtype=author&query=Suresh%2C+P">Pranav Suresh</a>, <a href="/search/q-bio?searchtype=author&query=Ghimire%2C+S">Santosh Ghimire</a>, <a href="/search/q-bio?searchtype=author&query=Calhoun%2C+V">Vince Calhoun</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Jingyu Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.15805v1-abstract-short" style="display: inline;"> Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimpl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.15805v1-abstract-full').style.display = 'inline'; document.getElementById('2405.15805v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.15805v1-abstract-full" style="display: none;"> Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM, leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix and provides evidence of goal-specific brain connectivity patterns, which opens up the potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.15805v1-abstract-full').style.display = 'none'; document.getElementById('2405.15805v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 Pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Medical Image Analysis, Volume 91, 2025, Article 102124 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.04726">arXiv:2404.04726</a> <span> [<a href="https://arxiv.org/pdf/2404.04726">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Rehman%2C+A">Azaan Rehman</a>, <a href="/search/q-bio?searchtype=author&query=Zhovmer%2C+A">Alexander Zhovmer</a>, <a href="/search/q-bio?searchtype=author&query=Sato%2C+R">Ryo Sato</a>, <a href="/search/q-bio?searchtype=author&query=Mukoyama%2C+Y">Yosuke Mukoyama</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiji Chen</a>, <a href="/search/q-bio?searchtype=author&query=Rissone%2C+A">Alberto Rissone</a>, <a href="/search/q-bio?searchtype=author&query=Puertollano%2C+R">Rosa Puertollano</a>, <a href="/search/q-bio?searchtype=author&query=Vishwasrao%2C+H">Harshad Vishwasrao</a>, <a href="/search/q-bio?searchtype=author&query=Shroff%2C+H">Hari Shroff</a>, <a href="/search/q-bio?searchtype=author&query=Combs%2C+C+A">Christian A. Combs</a>, <a href="/search/q-bio?searchtype=author&query=Xue%2C+H">Hui Xue</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.04726v1-abstract-short" style="display: inline;"> Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04726v1-abstract-full').style.display = 'inline'; document.getElementById('2404.04726v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.04726v1-abstract-full" style="display: none;"> Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based backbone model from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5-10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5 by 5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04726v1-abstract-full').style.display = 'none'; document.getElementById('2404.04726v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.03516">arXiv:2404.03516</a> <span> [<a href="https://arxiv.org/pdf/2404.03516">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Drug-target interaction prediction by integrating heterogeneous information with mutual attention network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yuanyuan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yingdong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+C">Chaoyong Wu</a>, <a href="/search/q-bio?searchtype=author&query=Zhana%2C+L">Lingmin Zhana</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+A">Aoyi Wang</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+C">Caiping Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+J">Jinzhong Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+W">Wuxia Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jianxin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+P">Peng Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.03516v1-abstract-short" style="display: inline;"> Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.03516v1-abstract-full').style.display = 'inline'; document.getElementById('2404.03516v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.03516v1-abstract-full" style="display: none;"> Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction. Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction. DrugMAN achieves the best prediction performance under four different scenarios, especially in real-world scenarios. DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.03516v1-abstract-full').style.display = 'none'; document.getElementById('2404.03516v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.08147">arXiv:2403.08147</a> <span> [<a href="https://arxiv.org/pdf/2403.08147">pdf</a>, <a href="https://arxiv.org/format/2403.08147">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Representing Molecules as Random Walks Over Interpretable Grammars </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+M">Michael Sun</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+M">Minghao Guo</a>, <a href="/search/q-bio?searchtype=author&query=Yuan%2C+W">Weize Yuan</a>, <a href="/search/q-bio?searchtype=author&query=Thost%2C+V">Veronika Thost</a>, <a href="/search/q-bio?searchtype=author&query=Owens%2C+C+E">Crystal Elaine Owens</a>, <a href="/search/q-bio?searchtype=author&query=Grosz%2C+A+F">Aristotle Franklin Grosz</a>, <a href="/search/q-bio?searchtype=author&query=Selvan%2C+S">Sharvaa Selvan</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+K">Katelyn Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Mohiuddin%2C+H">Hassan Mohiuddin</a>, <a href="/search/q-bio?searchtype=author&query=Pedretti%2C+B+J">Benjamin J Pedretti</a>, <a href="/search/q-bio?searchtype=author&query=Smith%2C+Z+P">Zachary P Smith</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jie Chen</a>, <a href="/search/q-bio?searchtype=author&query=Matusik%2C+W">Wojciech Matusik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.08147v3-abstract-short" style="display: inline;"> Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology. These applications often rely on more complex molecular structures with fewer examples that are carefully designed using known substructures. We propose a data-efficient and interpretable model for representin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08147v3-abstract-full').style.display = 'inline'; document.getElementById('2403.08147v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.08147v3-abstract-full" style="display: none;"> Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology. These applications often rely on more complex molecular structures with fewer examples that are carefully designed using known substructures. We propose a data-efficient and interpretable model for representing and reasoning over such molecules in terms of graph grammars that explicitly describe the hierarchical design space featuring motifs to be the design basis. We present a novel representation in the form of random walks over the design space, which facilitates both molecule generation and property prediction. We demonstrate clear advantages over existing methods in terms of performance, efficiency, and synthesizability of predicted molecules, and we provide detailed insights into the method's chemical interpretability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08147v3-abstract-full').style.display = 'none'; document.getElementById('2403.08147v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ICML 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.02724">arXiv:2403.02724</a> <span> [<a href="https://arxiv.org/pdf/2403.02724">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhan%2C+L">Lingmin Zhan</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yuanyuan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yingdong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+A">Aoyi Wang</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+C">Caiping Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+J">Jinzhong Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+W">Wuxia Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Lia%2C+P">Peng Lia</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jianxin Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.02724v1-abstract-short" style="display: inline;"> Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show that TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profile… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02724v1-abstract-full').style.display = 'inline'; document.getElementById('2403.02724v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.02724v1-abstract-full" style="display: none;"> Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show that TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference (RNAi), clustered regularly interspaced short palindromic repeat (CRISPR) and overexpression (OE). TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26,945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02724v1-abstract-full').style.display = 'none'; document.getElementById('2403.02724v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.02603">arXiv:2403.02603</a> <span> [<a href="https://arxiv.org/pdf/2403.02603">pdf</a>, <a href="https://arxiv.org/format/2403.02603">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Drug Resistance Predictions Based on a Directed Flag Transformer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+D">Dong Chen</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+G">Gengzhuo Liu</a>, <a href="/search/q-bio?searchtype=author&query=Du%2C+H">Hongyan Du</a>, <a href="/search/q-bio?searchtype=author&query=Jones%2C+B">Benjamin Jones</a>, <a href="/search/q-bio?searchtype=author&query=Wee%2C+J">Junjie Wee</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+R">Rui Wang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiahui Chen</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+J">Jana Shen</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+G">Guo-Wei Wei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.02603v2-abstract-short" style="display: inline;"> The continuous evolution of the SARS-CoV-2 virus poses a significant challenge to global public health. Of particular concern is the potential resistance to the widely prescribed drug PAXLOVID, of which the main ingredient nirmatrelvir inhibits the viral main protease (Mpro). Here, we developed CAPTURE (direCted flAg laPlacian Transformer for drUg Resistance prEdictions) to analyze the effects of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02603v2-abstract-full').style.display = 'inline'; document.getElementById('2403.02603v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.02603v2-abstract-full" style="display: none;"> The continuous evolution of the SARS-CoV-2 virus poses a significant challenge to global public health. Of particular concern is the potential resistance to the widely prescribed drug PAXLOVID, of which the main ingredient nirmatrelvir inhibits the viral main protease (Mpro). Here, we developed CAPTURE (direCted flAg laPlacian Transformer for drUg Resistance prEdictions) to analyze the effects of Mpro mutations on nirmatrelvir-Mpro binding affinities and identify potential drug-resistant mutations. CAPTURE combines a comprehensive mutation analysis with a resistance prediction module based on DFFormer-seq, which is a novel ensemble model that leverages a new Directed Flag Transformer and sequence embeddings from the protein and small-molecule-large-language models. Our analysis of the evolution of Mpro mutations revealed a progressive increase in mutation frequencies for residues near the binding site between May and December 2022, suggesting that the widespread use of PAXLOVID created a selective pressure that accelerated the evolution of drug-resistant variants. Applied to mutations at the nirmatrelvir-Mpro binding site, CAPTURE identified several potential resistance mutations, including H172Y and F140L, which have been experimentally confirmed, as well as five other mutations that await experimental verification. CAPTURE evaluation in a limited experimental data set on Mpro mutants gives a recall of 57\% and a precision of 71\% for predicting potential drug-resistant mutations. Our work establishes a powerful new framework for predicting drug-resistant mutations and real-time viral surveillance. The insights also guide the rational design of more resilient next-generation therapeutics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02603v2-abstract-full').style.display = 'none'; document.getElementById('2403.02603v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.00842">arXiv:2403.00842</a> <span> [<a href="https://arxiv.org/pdf/2403.00842">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Implementation of an AI-based MRD evaluation and prediction model for multiple myeloma </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jianfeng Chen</a>, <a href="/search/q-bio?searchtype=author&query=Xiong%2C+J">Jize Xiong</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yixu Wang</a>, <a href="/search/q-bio?searchtype=author&query=Xin%2C+Q">Qi Xin</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hong Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.00842v1-abstract-short" style="display: inline;"> With the application of hematopoietic stem cell transplantation and new drugs, the progression-free survival rate and overall survival rate of multiple myeloma have been greatly improved, but it is still considered as a kind of disease that cannot be completely cured. Many patients have disease recurrence after complete remission, which is rooted in the presence of minimal residual disease MRD in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00842v1-abstract-full').style.display = 'inline'; document.getElementById('2403.00842v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.00842v1-abstract-full" style="display: none;"> With the application of hematopoietic stem cell transplantation and new drugs, the progression-free survival rate and overall survival rate of multiple myeloma have been greatly improved, but it is still considered as a kind of disease that cannot be completely cured. Many patients have disease recurrence after complete remission, which is rooted in the presence of minimal residual disease MRD in patients. Studies have shown that positive MRD is an independent adverse prognostic factor affecting survival, so MRD detection is an important indicator to judge the prognosis of patients and guide clinical treatment. At present, multipa-rameter flow cytometry (MFC), polymerase chain reaction (PCR), positron emission tomography (positron emission) Several techniques, such as PET/computer tomography (CT), have been used for MRD detection of multiple myeloma.However, there is still no cure for the disease. "IFM2013-04" four clinical studies confirmed for the first time that proteasome inhibitors (PIs) and immunomodulatory drugs, The synergism and importance of the combination of IMiDs in the treatment of MM, the large Phase 3 clinical study SWOG SO777 compared the combination of bortezomib plus lenalidomide and dexamethasone. The efficacy of VRD and D established the status of VRD first-line treatment of MM, and due to the good efficacy of CD38 monoclonal antibody in large clinical studies, combination therapy with VRD has been recommended as the first-line treatment of MM. However, to explore the clinical value and problems of applying artificial intelligence bone marrow cell recognition system Morphogo in the detection of multiple myeloma minimal residual disease (MRD) <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00842v1-abstract-full').style.display = 'none'; document.getElementById('2403.00842v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.17209">arXiv:2402.17209</a> <span> [<a href="https://arxiv.org/pdf/2402.17209">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning-based Kinetic Analysis in Paper-based Analytical Cartridges Integrated with Field-effect Transistors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jang%2C+H">Hyun-June Jang</a>, <a href="/search/q-bio?searchtype=author&query=Joung%2C+H">Hyou-Arm Joung</a>, <a href="/search/q-bio?searchtype=author&query=Goncharov%2C+A">Artem Goncharov</a>, <a href="/search/q-bio?searchtype=author&query=Kanegusuku%2C+A+G">Anastasia Gant Kanegusuku</a>, <a href="/search/q-bio?searchtype=author&query=Chan%2C+C+W">Clarence W. Chan</a>, <a href="/search/q-bio?searchtype=author&query=Yeo%2C+K+J">Kiang-Teck Jerry Yeo</a>, <a href="/search/q-bio?searchtype=author&query=Zhuang%2C+W">Wen Zhuang</a>, <a href="/search/q-bio?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junhong Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.17209v1-abstract-short" style="display: inline;"> This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for sur… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17209v1-abstract-full').style.display = 'inline'; document.getElementById('2402.17209v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.17209v1-abstract-full" style="display: none;"> This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of < 6.46% and a decent concentration measurement correlation with an r2 value of > 0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies can create a new generation of FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17209v1-abstract-full').style.display = 'none'; document.getElementById('2402.17209v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.16894">arXiv:2402.16894</a> <span> [<a href="https://arxiv.org/pdf/2402.16894">pdf</a>, <a href="https://arxiv.org/format/2402.16894">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet Microscopy Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yao%2C+J">Jiachen Yao</a>, <a href="/search/q-bio?searchtype=author&query=Hagemann%2C+N">Nina Hagemann</a>, <a href="/search/q-bio?searchtype=author&query=Xiong%2C+Q">Qiaojie Xiong</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jianxu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Hermann%2C+D+M">Dirk M. Hermann</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+C">Chao Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.16894v1-abstract-short" style="display: inline;"> Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16894v1-abstract-full').style.display = 'inline'; document.getElementById('2402.16894v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.16894v1-abstract-full" style="display: none;"> Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods. Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16894v1-abstract-full').style.display = 'none'; document.getElementById('2402.16894v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11360">arXiv:2401.11360</a> <span> [<a href="https://arxiv.org/pdf/2401.11360">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhang%2C+R">Ruochi Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+H">Haoran Wu</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+C">Chang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+H">Huaping Li</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+Y">Yuqian Wu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+K">Kewei Li</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yifan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Deng%2C+Y">Yifan Deng</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiahui Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+F">Fengfeng Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+X">Xin Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.11360v1-abstract-short" style="display: inline;"> Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning fr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11360v1-abstract-full').style.display = 'inline'; document.getElementById('2401.11360v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11360v1-abstract-full" style="display: none;"> Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein Data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://github.com/zhangruochi/PepHarmony or http://www.healthinformaticslab.org/supp/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11360v1-abstract-full').style.display = 'none'; document.getElementById('2401.11360v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 5 figures, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.17670">arXiv:2312.17670</a> <span> [<a href="https://arxiv.org/pdf/2312.17670">pdf</a>, <a href="https://arxiv.org/format/2312.17670">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yang%2C+K">Kaiyuan Yang</a>, <a href="/search/q-bio?searchtype=author&query=Musio%2C+F">Fabio Musio</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Y">Yihui Ma</a>, <a href="/search/q-bio?searchtype=author&query=Juchler%2C+N">Norman Juchler</a>, <a href="/search/q-bio?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/q-bio?searchtype=author&query=Al-Maskari%2C+R">Rami Al-Maskari</a>, <a href="/search/q-bio?searchtype=author&query=H%C3%B6her%2C+L">Luciano H枚her</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+H+B">Hongwei Bran Li</a>, <a href="/search/q-bio?searchtype=author&query=Hamamci%2C+I+E">Ibrahim Ethem Hamamci</a>, <a href="/search/q-bio?searchtype=author&query=Sekuboyina%2C+A">Anjany Sekuboyina</a>, <a href="/search/q-bio?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+H">Houjing Huang</a>, <a href="/search/q-bio?searchtype=author&query=Prabhakar%2C+C">Chinmay Prabhakar</a>, <a href="/search/q-bio?searchtype=author&query=de+la+Rosa%2C+E">Ezequiel de la Rosa</a>, <a href="/search/q-bio?searchtype=author&query=Waldmannstetter%2C+D">Diana Waldmannstetter</a>, <a href="/search/q-bio?searchtype=author&query=Kofler%2C+F">Florian Kofler</a>, <a href="/search/q-bio?searchtype=author&query=Navarro%2C+F">Fernando Navarro</a>, <a href="/search/q-bio?searchtype=author&query=Menten%2C+M">Martin Menten</a>, <a href="/search/q-bio?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/q-bio?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/q-bio?searchtype=author&query=Vos%2C+I">Iris Vos</a>, <a href="/search/q-bio?searchtype=author&query=Ruigrok%2C+Y">Ynte Ruigrok</a>, <a href="/search/q-bio?searchtype=author&query=Velthuis%2C+B">Birgitta Velthuis</a>, <a href="/search/q-bio?searchtype=author&query=Kuijf%2C+H">Hugo Kuijf</a>, <a href="/search/q-bio?searchtype=author&query=H%C3%A4mmerli%2C+J">Julien H盲mmerli</a> , et al. (59 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.17670v3-abstract-short" style="display: inline;"> The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modaliti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17670v3-abstract-full').style.display = 'inline'; document.getElementById('2312.17670v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.17670v3-abstract-full" style="display: none;"> The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17670v3-abstract-full').style.display = 'none'; document.getElementById('2312.17670v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 11 figures, 9 tables. Summary Paper for the MICCAI TopCoW 2023 Challenge</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.03475">arXiv:2312.03475</a> <span> [<a href="https://arxiv.org/pdf/2312.03475">pdf</a>, <a href="https://arxiv.org/format/2312.03475">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Du%2C+W">Weitao Du</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiujiu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+X">Xuecang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zhiming Ma</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+S">Shengchao Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.03475v1-abstract-short" style="display: inline;"> Recently, artificial intelligence for drug discovery has raised increasing interest in both machine learning and chemistry domains. The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery. In this work, we propose a pretraining method for molecu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03475v1-abstract-full').style.display = 'inline'; document.getElementById('2312.03475v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.03475v1-abstract-full" style="display: none;"> Recently, artificial intelligence for drug discovery has raised increasing interest in both machine learning and chemistry domains. The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery. In this work, we propose a pretraining method for molecule joint auto-encoding (MoleculeJAE). MoleculeJAE can learn both the 2D bond (topology) and 3D conformation (geometry) information, and a diffusion process model is applied to mimic the augmented trajectories of such two modalities, based on which, MoleculeJAE will learn the inherent chemical structure in a self-supervised manner. Thus, the pretrained geometrical representation in MoleculeJAE is expected to benefit downstream geometry-related tasks. Empirically, MoleculeJAE proves its effectiveness by reaching state-of-the-art performance on 15 out of 20 tasks by comparing it with 12 competitive baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03475v1-abstract-full').style.display = 'none'; document.getElementById('2312.03475v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">NeurIPS 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.03520">arXiv:2311.03520</a> <span> [<a href="https://arxiv.org/pdf/2311.03520">pdf</a>, <a href="https://arxiv.org/format/2311.03520">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.media.2024.103433">10.1016/j.media.2024.103433 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Thapaliya%2C+B">Bishal Thapaliya</a>, <a href="/search/q-bio?searchtype=author&query=Akbas%2C+E">Esra Akbas</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiayu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Sapkota%2C+R">Raam Sapkota</a>, <a href="/search/q-bio?searchtype=author&query=Ray%2C+B">Bhaskar Ray</a>, <a href="/search/q-bio?searchtype=author&query=Suresh%2C+P">Pranav Suresh</a>, <a href="/search/q-bio?searchtype=author&query=Calhoun%2C+V">Vince Calhoun</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Jingyu Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.03520v3-abstract-short" style="display: inline;"> Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystalli… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03520v3-abstract-full').style.display = 'inline'; document.getElementById('2311.03520v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.03520v3-abstract-full" style="display: none;"> Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized, and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03520v3-abstract-full').style.display = 'none'; document.getElementById('2311.03520v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Medical Image Analysis, Volume 90, 2024, Article 102123 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.00850">arXiv:2311.00850</a> <span> [<a href="https://arxiv.org/pdf/2311.00850">pdf</a>, <a href="https://arxiv.org/format/2311.00850">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> EMPOT: partial alignment of density maps and rigid body fitting using unbalanced Gromov-Wasserstein divergence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Riahi%2C+A+T">Aryan Tajmir Riahi</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+C">Chenwei Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">James Chen</a>, <a href="/search/q-bio?searchtype=author&query=Condon%2C+A">Anne Condon</a>, <a href="/search/q-bio?searchtype=author&query=Duc%2C+K+D">Khanh Dao Duc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.00850v1-abstract-short" style="display: inline;"> Aligning EM density maps and fitting atomic models are essential steps in single particle cryogenic electron microscopy (cryo-EM), with recent methods leveraging various algorithms and machine learning tools. As aligning maps remains challenging in the presence of a map that only partially fits the other (e.g. one subunit), we here propose a new procedure, EMPOT (EM Partial alignment with Optimal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00850v1-abstract-full').style.display = 'inline'; document.getElementById('2311.00850v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.00850v1-abstract-full" style="display: none;"> Aligning EM density maps and fitting atomic models are essential steps in single particle cryogenic electron microscopy (cryo-EM), with recent methods leveraging various algorithms and machine learning tools. As aligning maps remains challenging in the presence of a map that only partially fits the other (e.g. one subunit), we here propose a new procedure, EMPOT (EM Partial alignment with Optimal Transport), for partial alignment of 3D maps. EMPOT first finds a coupling between 3D point-cloud representations, which is associated with their so-called unbalanced Gromov Wasserstein divergence, and second, uses this coupling to find an optimal rigid body transformation. Upon running and benchmarking our method with experimental maps and structures, we show that EMPOT outperforms standard methods for aligning subunits of a protein complex and fitting atomic models to a density map, suggesting potential applications of Partial Optimal Transport for improving Cryo-EM pipelines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00850v1-abstract-full').style.display = 'none'; document.getElementById('2311.00850v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.18760">arXiv:2310.18760</a> <span> [<a href="https://arxiv.org/pdf/2310.18760">pdf</a>, <a href="https://arxiv.org/format/2310.18760">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Algebraic Topology">math.AT</span> </div> </div> <p class="title is-5 mathjax"> Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wee%2C+J">JunJie Wee</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiahui Chen</a>, <a href="/search/q-bio?searchtype=author&query=Xia%2C+K">Kelin Xia</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+G">Guo-Wei Wei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.18760v2-abstract-short" style="display: inline;"> Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.18760v2-abstract-full').style.display = 'inline'; document.getElementById('2310.18760v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.18760v2-abstract-full" style="display: none;"> Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild-type and mutant proteins. This work integrates persistent Laplacians and pre-trained Transformer for the task. The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types. Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.18760v2-abstract-full').style.display = 'none'; document.getElementById('2310.18760v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.06191">arXiv:2310.06191</a> <span> [<a href="https://arxiv.org/pdf/2310.06191">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Investigating the Correlation between Force Output, Strains, and Pressure for Active Skeletal Muscle Contractions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Taneja%2C+K">Karan Taneja</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+X">Xiaolong He</a>, <a href="/search/q-bio?searchtype=author&query=Hodgson%2C+J">John Hodgson</a>, <a href="/search/q-bio?searchtype=author&query=Sinha%2C+U">Usha Sinha</a>, <a href="/search/q-bio?searchtype=author&query=Sinha%2C+S">Shantanu Sinha</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J+S">J. S. Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.06191v1-abstract-short" style="display: inline;"> Experimental observations suggest that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure generated by the muscle belly. However, pressure often proves difficult to measure through in-vivo tests. Simulations on the other hand, offer a tool to model muscle contractions and analyze the relationship between muscle force generation and deformations as well… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06191v1-abstract-full').style.display = 'inline'; document.getElementById('2310.06191v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.06191v1-abstract-full" style="display: none;"> Experimental observations suggest that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure generated by the muscle belly. However, pressure often proves difficult to measure through in-vivo tests. Simulations on the other hand, offer a tool to model muscle contractions and analyze the relationship between muscle force generation and deformations as well as pressure outputs, enabling us to gain insight into correlations among experimentally measurable quantities such as principal and volumetric strains, and the force output. In this work, a correlation study is performed using Pearson's and Spearman's correlation coefficients on the force output of the skeletal muscle, the principal and volumetric strains experienced by the muscle and the pressure developed within the muscle belly as the muscle tissue undergoes isometric contractions due to varying activation profiles. The study reveals strong correlations between force output and the strains at all locations of the belly, irrespective of the type of activation profile used. This observation enables estimation on the contribution of various muscle groups to the total force by the experimentally measurable principal and volumetric strains in the muscle belly. It is also observed that pressure does not correlate well with force output due to stress relaxation near the boundary of muscle belly. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06191v1-abstract-full').style.display = 'none'; document.getElementById('2310.06191v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.07701">arXiv:2309.07701</a> <span> [<a href="https://arxiv.org/pdf/2309.07701">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Semantic reconstruction of continuous language from MEG signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+B">Bo Wang</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+X">Xiran Xu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+L">Longxiang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Xiao%2C+B">Boda Xiao</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+X">Xihong Wu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jing Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.07701v1-abstract-short" style="display: inline;"> Decoding language from neural signals holds considerable theoretical and practical importance. Previous research has indicated the feasibility of decoding text or speech from invasive neural signals. However, when using non-invasive neural signals, significant challenges are encountered due to their low quality. In this study, we proposed a data-driven approach for decoding semantic of language fr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.07701v1-abstract-full').style.display = 'inline'; document.getElementById('2309.07701v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.07701v1-abstract-full" style="display: none;"> Decoding language from neural signals holds considerable theoretical and practical importance. Previous research has indicated the feasibility of decoding text or speech from invasive neural signals. However, when using non-invasive neural signals, significant challenges are encountered due to their low quality. In this study, we proposed a data-driven approach for decoding semantic of language from Magnetoencephalography (MEG) signals recorded while subjects were listening to continuous speech. First, a multi-subject decoding model was trained using contrastive learning to reconstruct continuous word embeddings from MEG data. Subsequently, a beam search algorithm was adopted to generate text sequences based on the reconstructed word embeddings. Given a candidate sentence in the beam, a language model was used to predict the subsequent words. The word embeddings of the subsequent words were correlated with the reconstructed word embedding. These correlations were then used as a measure of the probability for the next word. The results showed that the proposed continuous word embedding model can effectively leverage both subject-specific and subject-shared information. Additionally, the decoded text exhibited significant similarity to the target text, with an average BERTScore of 0.816, a score comparable to that in the previous fMRI study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.07701v1-abstract-full').style.display = 'none'; document.getElementById('2309.07701v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.01788">arXiv:2309.01788</a> <span> [<a href="https://arxiv.org/pdf/2309.01788">pdf</a>, <a href="https://arxiv.org/format/2309.01788">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Guo%2C+M">Minghao Guo</a>, <a href="/search/q-bio?searchtype=author&query=Thost%2C+V">Veronika Thost</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+S+W">Samuel W Song</a>, <a href="/search/q-bio?searchtype=author&query=Balachandran%2C+A">Adithya Balachandran</a>, <a href="/search/q-bio?searchtype=author&query=Das%2C+P">Payel Das</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jie Chen</a>, <a href="/search/q-bio?searchtype=author&query=Matusik%2C+W">Wojciech Matusik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.01788v1-abstract-short" style="display: inline;"> The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the wealth of recent literature. Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of manual extraction from literature and laborious experimentation. In this work, w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01788v1-abstract-full').style.display = 'inline'; document.getElementById('2309.01788v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.01788v1-abstract-full" style="display: none;"> The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the wealth of recent literature. Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of manual extraction from literature and laborious experimentation. In this work, we propose a data-efficient property predictor by utilizing a learnable hierarchical molecular grammar that can generate molecules from grammar production rules. Such a grammar induces an explicit geometry of the space of molecular graphs, which provides an informative prior on molecular structural similarity. The property prediction is performed using graph neural diffusion over the grammar-induced geometry. On both small and large datasets, our evaluation shows that this approach outperforms a wide spectrum of baselines, including supervised and pre-trained graph neural networks. We include a detailed ablation study and further analysis of our solution, showing its effectiveness in cases with extremely limited data. Code is available at https://github.com/gmh14/Geo-DEG. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01788v1-abstract-full').style.display = 'none'; document.getElementById('2309.01788v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 10 figures; ICML 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.15474">arXiv:2308.15474</a> <span> [<a href="https://arxiv.org/pdf/2308.15474">pdf</a>, <a href="https://arxiv.org/format/2308.15474">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> A General-Purpose Self-Supervised Model for Computational Pathology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+R+J">Richard J. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+T">Tong Ding</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+M+Y">Ming Y. Lu</a>, <a href="/search/q-bio?searchtype=author&query=Williamson%2C+D+F+K">Drew F. K. Williamson</a>, <a href="/search/q-bio?searchtype=author&query=Jaume%2C+G">Guillaume Jaume</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+B">Bowen Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+A">Andrew Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Shao%2C+D">Daniel Shao</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+A+H">Andrew H. Song</a>, <a href="/search/q-bio?searchtype=author&query=Shaban%2C+M">Muhammad Shaban</a>, <a href="/search/q-bio?searchtype=author&query=Williams%2C+M">Mane Williams</a>, <a href="/search/q-bio?searchtype=author&query=Vaidya%2C+A">Anurag Vaidya</a>, <a href="/search/q-bio?searchtype=author&query=Sahai%2C+S">Sharifa Sahai</a>, <a href="/search/q-bio?searchtype=author&query=Oldenburg%2C+L">Lukas Oldenburg</a>, <a href="/search/q-bio?searchtype=author&query=Weishaupt%2C+L+L">Luca L. Weishaupt</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J+J">Judy J. Wang</a>, <a href="/search/q-bio?searchtype=author&query=Williams%2C+W">Walt Williams</a>, <a href="/search/q-bio?searchtype=author&query=Le%2C+L+P">Long Phi Le</a>, <a href="/search/q-bio?searchtype=author&query=Gerber%2C+G">Georg Gerber</a>, <a href="/search/q-bio?searchtype=author&query=Mahmood%2C+F">Faisal Mahmood</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.15474v1-abstract-short" style="display: inline;"> Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.15474v1-abstract-full').style.display = 'inline'; document.getElementById('2308.15474v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.15474v1-abstract-full" style="display: none;"> Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.15474v1-abstract-full').style.display = 'none'; document.getElementById('2308.15474v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.15116">arXiv:2308.15116</a> <span> [<a href="https://arxiv.org/pdf/2308.15116">pdf</a>, <a href="https://arxiv.org/format/2308.15116">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jingbang Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yian Wang</a>, <a href="/search/q-bio?searchtype=author&query=Qu%2C+X">Xingwei Qu</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+S">Shuangjia Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Y">Yaodong Yang</a>, <a href="/search/q-bio?searchtype=author&query=Dong%2C+H">Hao Dong</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+J">Jie Fu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.15116v3-abstract-short" style="display: inline;"> Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.15116v3-abstract-full').style.display = 'inline'; document.getElementById('2308.15116v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.15116v3-abstract-full" style="display: none;"> Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.15116v3-abstract-full').style.display = 'none'; document.getElementById('2308.15116v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.06288">arXiv:2308.06288</a> <span> [<a href="https://arxiv.org/pdf/2308.06288">pdf</a>, <a href="https://arxiv.org/format/2308.06288">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Jiayuan Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yu Wang</a>, <a href="/search/q-bio?searchtype=author&query=Deng%2C+R">Ruining Deng</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Q">Quan Liu</a>, <a href="/search/q-bio?searchtype=author&query=Cui%2C+C">Can Cui</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+T">Tianyuan Yao</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Y">Yilin Liu</a>, <a href="/search/q-bio?searchtype=author&query=Zhong%2C+J">Jianyong Zhong</a>, <a href="/search/q-bio?searchtype=author&query=Fogo%2C+A+B">Agnes B. Fogo</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+H">Haichun Yang</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+S">Shilin Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Huo%2C+Y">Yuankai Huo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.06288v1-abstract-short" style="display: inline;"> Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.06288v1-abstract-full').style.display = 'inline'; document.getElementById('2308.06288v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.06288v1-abstract-full" style="display: none;"> Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features.The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The implementation and its complete source code of the toolkit are made openly accessible at https://github.com/hrlblab/spatial_pathomics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.06288v1-abstract-full').style.display = 'none'; document.getElementById('2308.06288v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Chen%2C+J&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Chen%2C+J&start=0" class="pagination-link 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