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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.20513">arXiv:2503.20513</a> <span> [<a href="https://arxiv.org/pdf/2503.20513">pdf</a>, <a href="https://arxiv.org/format/2503.20513">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="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> A Principal Submanifold-based Approach for Clustering and Multiscale RNA Correction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wu%2C+M">Menghao Wu</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Z">Zhigang 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="2503.20513v1-abstract-short" style="display: inline;"> RNA structure determination is essential for understanding its biological functions. However, the reconstruction process often faces challenges, such as atomic clashes, which can lead to inaccurate models. To address these challenges, we introduce the principal submanifold (PSM) approach for analyzing RNA data on a torus. This method provides an accurate, low-dimensional feature representation, ov… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.20513v1-abstract-full').style.display = 'inline'; document.getElementById('2503.20513v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.20513v1-abstract-full" style="display: none;"> RNA structure determination is essential for understanding its biological functions. However, the reconstruction process often faces challenges, such as atomic clashes, which can lead to inaccurate models. To address these challenges, we introduce the principal submanifold (PSM) approach for analyzing RNA data on a torus. This method provides an accurate, low-dimensional feature representation, overcoming the limitations of previous torus-based methods. By combining PSM with DBSCAN, we propose a novel clustering technique, the principal submanifold-based DBSCAN (PSM-DBSCAN). Our approach achieves superior clustering accuracy and increased robustness to noise. Additionally, we apply this new method for multiscale corrections, effectively resolving RNA backbone clashes at both microscopic and mesoscopic scales. Extensive simulations and comparative studies highlight the enhanced precision and scalability of our method, demonstrating significant improvements over existing approaches. The proposed methodology offers a robust foundation for correcting complex RNA structures and has broad implications for applications in structural biology and bioinformatics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.20513v1-abstract-full').style.display = 'none'; document.getElementById('2503.20513v1-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 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">28 pages, 15 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/2503.13522">arXiv:2503.13522</a> <span> [<a href="https://arxiv.org/pdf/2503.13522">pdf</a>, <a href="https://arxiv.org/ps/2503.13522">ps</a>, <a href="https://arxiv.org/format/2503.13522">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="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> <p class="title is-5 mathjax"> Advanced Deep Learning Methods for Protein Structure Prediction and Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+T">Tianyang Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yichao Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Deng%2C+N">Ningyuan Deng</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+X">Xinyuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Bi%2C+Z">Ziqian Bi</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Z">Zheyu Yao</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Keyu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/q-bio?searchtype=author&query=Niu%2C+Q">Qian Niu</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Junyu Liu</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+B">Benji Peng</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+S">Sen Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+M">Ming Liu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+L">Li Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Pan%2C+X">Xuanhe Pan</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Jinlang Wang</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+P">Pohsun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Wen%2C+Y">Yizhu Wen</a>, <a href="/search/q-bio?searchtype=author&query=Yan%2C+L+K">Lawrence KQ Yan</a>, <a href="/search/q-bio?searchtype=author&query=Tseng%2C+H">Hongming Tseng</a>, <a href="/search/q-bio?searchtype=author&query=Zhong%2C+Y">Yan Zhong</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yunze Wang</a>, <a href="/search/q-bio?searchtype=author&query=Qin%2C+Z">Ziyuan Qin</a>, <a href="/search/q-bio?searchtype=author&query=Jing%2C+B">Bowen Jing</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+J">Junjie Yang</a> , et al. (3 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="2503.13522v2-abstract-short" style="display: inline;"> After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13522v2-abstract-full').style.display = 'inline'; document.getElementById('2503.13522v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.13522v2-abstract-full" style="display: none;"> After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13522v2-abstract-full').style.display = 'none'; document.getElementById('2503.13522v2-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> 21 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.09635">arXiv:2408.09635</a> <span> [<a href="https://arxiv.org/pdf/2408.09635">pdf</a>, <a href="https://arxiv.org/format/2408.09635">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="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Moghaddam%2C+A+H">Arya Hadizadeh Moghaddam</a>, <a href="/search/q-bio?searchtype=author&query=Kerdabadi%2C+M+N">Mohsen Nayebi Kerdabadi</a>, <a href="/search/q-bio?searchtype=author&query=Zhong%2C+C">Cuncong Zhong</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Z">Zijun 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="2408.09635v1-abstract-short" style="display: inline;"> Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodologies such as deep neural networks for sophisticated analysis. To address this "small data" dilemma, Meta-Learning has been introduced as a solution to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09635v1-abstract-full').style.display = 'inline'; document.getElementById('2408.09635v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.09635v1-abstract-full" style="display: none;"> Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodologies such as deep neural networks for sophisticated analysis. To address this "small data" dilemma, Meta-Learning has been introduced as a solution to enhance the optimization of machine learning models by utilizing similar datasets, thereby facilitating a quicker adaptation to target datasets without the requirement of sufficient samples. In this study, we present a meta-learning-based approach for predicting lung cancer from gene expression profiles. We apply this framework to well-established deep learning methodologies and employ four distinct datasets for the meta-learning tasks, where one as the target dataset and the rest as source datasets. Our approach is evaluated against both traditional and deep learning methodologies, and the results show the superior performance of meta-learning on augmented source data compared to the baselines trained on single datasets. Moreover, we conduct the comparative analysis between meta-learning and transfer learning methodologies to highlight the efficiency of the proposed approach in addressing the challenges associated with limited sample sizes. Finally, we incorporate the explainability study to illustrate the distinctiveness of decisions made by meta-learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09635v1-abstract-full').style.display = 'none'; document.getElementById('2408.09635v1-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> 18 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">Accepted to AMIA 2024 Annual Symposium</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.15364">arXiv:2205.15364</a> <span> [<a href="https://arxiv.org/pdf/2205.15364">pdf</a>, <a href="https://arxiv.org/format/2205.15364">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 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/10.1049/cit2.12194">10.1049/cit2.12194 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Associative Learning Mechanism for Drug-Target Interaction Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Z">Zhiqin Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Z">Zheng Yao</a>, <a href="/search/q-bio?searchtype=author&query=Qi%2C+G">Guanqiu Qi</a>, <a href="/search/q-bio?searchtype=author&query=Mazur%2C+N">Neal Mazur</a>, <a href="/search/q-bio?searchtype=author&query=Cong%2C+B">Baisen Cong</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="2205.15364v5-abstract-short" style="display: inline;"> As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. Although deep learning has been applied to DTA-related research, existing soluti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.15364v5-abstract-full').style.display = 'inline'; document.getElementById('2205.15364v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.15364v5-abstract-full" style="display: none;"> As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. Although deep learning has been applied to DTA-related research, existing solutions ignore fundamental correlations between molecular substructures in molecular representation learning of drug compound molecules/protein targets. Moreover, traditional methods lack the interpretability of the DTA prediction process. This results in missing feature information of intermolecular interactions, thereby affecting prediction performance. Therefore, this paper proposes a DTA prediction method with interactive learning and an autoencoder mechanism. The proposed model enhances the corresponding ability to capture the feature information of a single molecular sequence by the drug/protein molecular representation learning module and supplements the information interaction between molecular sequence pairs by the interactive information learning module. The DTA value prediction module fuses the drug-target pair interaction information to output the predicted value of DTA. Additionally, this paper theoretically proves that the proposed method maximizes evidence lower bound (ELBO) for the joint distribution of the DTA prediction model, which enhances the consistency of the probability distribution between the actual value and the predicted value. The experimental results confirm mutual transformer-drug target affinity (MT-DTA) achieves better performance than other comparative methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.15364v5-abstract-full').style.display = 'none'; document.getElementById('2205.15364v5-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </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">The extended and final version of this paper has been published with open access modality in the CAAI Transactions on Intelligence Technology and can be found at link LINK HERE. Please refer to the TRIT published version in your scientific papers</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Zhiqin Zhu (2023) 1-20 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.00269">arXiv:2106.00269</a> <span> [<a href="https://arxiv.org/pdf/2106.00269">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Other Quantitative Biology">q-bio.OT</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/10.1007/s41365-020-00777-8">10.1007/s41365-020-00777-8 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Study on the multiple characteristics of M3 generation of pea mutants obtained by neutron irradiation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xu%2C+D">Dapeng Xu</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Z">Ze'en Yao</a>, <a href="/search/q-bio?searchtype=author&query=Pan%2C+J">Jianbin Pan</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+H">Huyuan Feng</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+Z">Zhiqi Guo</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+X">Xiaolong Lu</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="2106.00269v1-abstract-short" style="display: inline;"> Irradiation breeding is an important technique in the effort to solve food shortages and improve the quality of agricultural products. In this study, a field test was implemented on the M3 generation of two mutant pea plants gained from previous neutron radiation of pea seeds. The relationship between agronomic characteristics and yields of the mutants was investigated. Moreover, differences in ph… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.00269v1-abstract-full').style.display = 'inline'; document.getElementById('2106.00269v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.00269v1-abstract-full" style="display: none;"> Irradiation breeding is an important technique in the effort to solve food shortages and improve the quality of agricultural products. In this study, a field test was implemented on the M3 generation of two mutant pea plants gained from previous neutron radiation of pea seeds. The relationship between agronomic characteristics and yields of the mutants was investigated. Moreover, differences in physiological and biochemical properties and seed nutrients were analyzed. The results demonstrated that the plant height, effective pods per plant, and yield per plant of mutant Leaf-M1 were 45.0%, 43.2%, and 50.9% higher than those of the control group. Further analysis attributed the increase in yield per plant to the increased branching number. The yield per plant of mutant Leaf-M2 was 7.8% higher than that of the control group, which could be related with the increased chlorophyll content in the leaves. There was a significant difference between the two mutants in the increase of yield per plant owing to morphological variation between the two mutants. There were significant differences in SOD activity and MDA content between the two mutants and the control, indicating that the physiological regulation of the two mutants also changed. In addition, the iron element content of seeds of the two mutants were about 10.9% lower than in the seeds of the control group, a significant difference. These findings indicate that the mutants Leaf-M1 and Leaf-M2 have breeding value and material value for molecular biological studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.00269v1-abstract-full').style.display = 'none'; document.getElementById('2106.00269v1-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 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </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">13 pages,3 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nuclear Science and Techniques, 2020,31:67 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.09283">arXiv:2002.09283</a> <span> [<a href="https://arxiv.org/pdf/2002.09283">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Digital Libraries">cs.DL</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="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/10.1038/s41597-022-01211-x">10.1038/s41597-022-01211-x <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Cai%2C+H">Hanshu Cai</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+Y">Yiwen Gao</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+S">Shuting Sun</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+N">Na Li</a>, <a href="/search/q-bio?searchtype=author&query=Tian%2C+F">Fuze Tian</a>, <a href="/search/q-bio?searchtype=author&query=Xiao%2C+H">Han Xiao</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+J">Jianxiu Li</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Z">Zhengwu Yang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xiaowei Li</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Q">Qinglin Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Z">Zhenyu Liu</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Z">Zhijun Yao</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+M">Minqiang Yang</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+H">Hong Peng</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+J">Jing Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+X">Xiaowei Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+G">Guoping Gao</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+F">Fang Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+Z">Zhihua Guo</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+R">Rong Ma</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+J">Jing Yang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+L">Lan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+X">Xiping Hu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yumin Li</a> , et al. (1 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="2002.09283v3-abstract-short" style="display: inline;"> According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.09283v3-abstract-full').style.display = 'inline'; document.getElementById('2002.09283v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.09283v3-abstract-full" style="display: none;"> According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.09283v3-abstract-full').style.display = 'none'; document.getElementById('2002.09283v3-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 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Sci Data 9, 178 (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.05663">arXiv:1911.05663</a> <span> [<a href="https://arxiv.org/pdf/1911.05663">pdf</a>, <a href="https://arxiv.org/format/1911.05663">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> A coupled autoencoder approach for multi-modal analysis of cell types </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Gala%2C+R">Rohan Gala</a>, <a href="/search/q-bio?searchtype=author&query=Gouwens%2C+N">Nathan Gouwens</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Z">Zizhen Yao</a>, <a href="/search/q-bio?searchtype=author&query=Budzillo%2C+A">Agata Budzillo</a>, <a href="/search/q-bio?searchtype=author&query=Penn%2C+O">Osnat Penn</a>, <a href="/search/q-bio?searchtype=author&query=Tasic%2C+B">Bosiljka Tasic</a>, <a href="/search/q-bio?searchtype=author&query=Murphy%2C+G">Gabe Murphy</a>, <a href="/search/q-bio?searchtype=author&query=Zeng%2C+H">Hongkui Zeng</a>, <a href="/search/q-bio?searchtype=author&query=S%C3%BCmb%C3%BCl%2C+U">Uygar S眉mb眉l</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="1911.05663v1-abstract-short" style="display: inline;"> Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types. While clustering of neuron populations based on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.05663v1-abstract-full').style.display = 'inline'; document.getElementById('1911.05663v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.05663v1-abstract-full" style="display: none;"> Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types. While clustering of neuron populations based on a particular data modality can be used to define cell types, such definitions are often inconsistent across different characterization modalities. We pose this issue of cross-modal alignment as an optimization problem and develop an approach based on coupled training of autoencoders as a framework for such analyses. We apply this framework to a Patch-seq dataset consisting of transcriptomic and electrophysiological profiles for the same set of neurons to study consistency of representations across modalities, and evaluate cross-modal data prediction ability. We explore the problem where only a subset of neurons is characterized with more than one modality, and demonstrate that representations learned by coupled autoencoders can be used to identify types sampled only by a single modality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.05663v1-abstract-full').style.display = 'none'; document.getElementById('1911.05663v1-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 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </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">Main text : 10 pages, 5 figures. Supp text : 6 pages, 3 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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