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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Chen, K"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small 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/2503.13522">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/2503.09251">arXiv:2503.09251</a> <span> [<a href="https://arxiv.org/pdf/2503.09251">pdf</a>, <a href="https://arxiv.org/format/2503.09251">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"> SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yigang Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ji%2C+X">Xiang Ji</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Z">Ziyue Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+Y">Yuming Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+Y">Yang-Chi-Dung Lin</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+H">Hsi-Yuan Huang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+T">Tao Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Lai%2C+Y">Yi Lai</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Ke Chen</a>, <a href="/search/q-bio?searchtype=author&query=Su%2C+C">Chang Su</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+X">Xingqiao Lin</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Z">Zihao Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yanggyi Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+K">Kangping Wei</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+J">Jiehui Fu</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Yixian Huang</a>, <a href="/search/q-bio?searchtype=author&query=Cui%2C+S">Shidong Cui</a>, <a href="/search/q-bio?searchtype=author&query=Yen%2C+S">Shih-Chung Yen</a>, <a href="/search/q-bio?searchtype=author&query=Warshel%2C+A">Ariel Warshel</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+H">Hsien-Da Huang</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="2503.09251v1-abstract-short" style="display: inline;"> Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To address these challenges, we propose SCOPE-DTI, a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advanced deep learning modeling. Constructed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.09251v1-abstract-full').style.display = 'inline'; document.getElementById('2503.09251v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.09251v1-abstract-full" style="display: none;"> Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To address these challenges, we propose SCOPE-DTI, a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advanced deep learning modeling. Constructed from 13 public repositories, the SCOPE dataset expands data volume by up to 100-fold compared to common benchmarks such as the Human dataset. The SCOPE model integrates three-dimensional protein and compound representations, graph neural networks, and bilinear attention mechanisms to effectively capture cross domain interaction patterns, significantly outperforming state-of-the-art methods across various DTI prediction tasks. Additionally, SCOPE-DTI provides a user-friendly interface and database. We further validate its effectiveness by experimentally identifying anticancer targets of Ginsenoside Rh1. By offering comprehensive data, advanced modeling, and accessible tools, SCOPE-DTI accelerates drug discovery research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.09251v1-abstract-full').style.display = 'none'; document.getElementById('2503.09251v1-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> 12 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/2502.03478">arXiv:2502.03478</a> <span> [<a href="https://arxiv.org/pdf/2502.03478">pdf</a>, <a href="https://arxiv.org/ps/2502.03478">ps</a>, <a href="https://arxiv.org/format/2502.03478">other</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> <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"> From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings </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=Chen%2C+S">Silin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yunze Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yichao Zhang</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=Liu%2C+M">Ming Liu</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=Feng%2C+P">Pohsun Feng</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+X">Xintian Sun</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+C">Charles Zhang</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=Fei%2C+C">Cheng Fei</a>, <a href="/search/q-bio?searchtype=author&query=Yan%2C+L+K">Lawrence KQ Yan</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="2502.03478v1-abstract-short" style="display: inline;"> The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and regulatory networks, but these predictions must be validated through experimental… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03478v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03478v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03478v1-abstract-full" style="display: none;"> The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and regulatory networks, but these predictions must be validated through experimental approaches to ensure their biological relevance. This review explores the various methods and technologies used for experimental validation, including gene expression analysis, protein-protein interaction verification, and pathway validation. We also discuss the challenges involved in translating computational predictions to experimental settings and highlight the importance of collaboration between bioinformatics and experimental research. Finally, emerging technologies, such as CRISPR gene editing, next-generation sequencing, and artificial intelligence, are shaping the future of bioinformatics validation and driving more accurate and efficient biological discoveries. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03478v1-abstract-full').style.display = 'none'; document.getElementById('2502.03478v1-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 January, 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">16 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.02146">arXiv:2501.02146</a> <span> [<a href="https://arxiv.org/pdf/2501.02146">pdf</a>, <a href="https://arxiv.org/format/2501.02146">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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yanxi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Su%2C+Y">Yi Su</a>, <a href="/search/q-bio?searchtype=author&query=Dumitrascu%2C+C">Celine Dumitrascu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kewei Chen</a>, <a href="/search/q-bio?searchtype=author&query=Weidman%2C+D">David Weidman</a>, <a href="/search/q-bio?searchtype=author&query=Caselli%2C+R+J">Richard J Caselli</a>, <a href="/search/q-bio?searchtype=author&query=Ashton%2C+N">Nicholas Ashton</a>, <a href="/search/q-bio?searchtype=author&query=Reiman%2C+E+M">Eric M Reiman</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yalin 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="2501.02146v2-abstract-short" style="display: inline;"> Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. Blood-based biomarkers (BBBMs) are revolutionizing Alzheimer's disease (AD) detection by identifying patients and quantifying brain amyloid levels. However, the potential of BBBMs to enhance PET image synthesis remains unexplored. In this paper, we performed a thorough… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.02146v2-abstract-full').style.display = 'inline'; document.getElementById('2501.02146v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.02146v2-abstract-full" style="display: none;"> Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. Blood-based biomarkers (BBBMs) are revolutionizing Alzheimer's disease (AD) detection by identifying patients and quantifying brain amyloid levels. However, the potential of BBBMs to enhance PET image synthesis remains unexplored. In this paper, we performed a thorough study on the effect of incorporating BBBM into deep generative models. By evaluating three widely used cross-modality translation models, we found that BBBMs integration consistently enhances the generative quality across all models. By visual inspection of the generated results, we observed that PET images generated by CycleGAN exhibit the best visual fidelity. Based on these findings, we propose Plasma-CycleGAN, a novel generative model based on CycleGAN, to synthesize PET images from MRI using BBBMs as conditions. This is the first approach to integrate BBBMs in conditional cross-modality translation between MRI and PET. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.02146v2-abstract-full').style.display = 'none'; document.getElementById('2501.02146v2-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 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">Accepted by ISBI 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.00473">arXiv:2410.00473</a> <span> [<a href="https://arxiv.org/pdf/2410.00473">pdf</a>, <a href="https://arxiv.org/format/2410.00473">other</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> <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"> Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Hui Ma</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kai 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="2410.00473v1-abstract-short" style="display: inline;"> Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcriptomic landscapes in two or three dimensions to depict biological populations accurately. However, t-SNE often fails to account for uncertainties in the original dataset, leading to misleading visualizations where cell subsets with noise appear indistingu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.00473v1-abstract-full').style.display = 'inline'; document.getElementById('2410.00473v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.00473v1-abstract-full" style="display: none;"> Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcriptomic landscapes in two or three dimensions to depict biological populations accurately. However, t-SNE often fails to account for uncertainties in the original dataset, leading to misleading visualizations where cell subsets with noise appear indistinguishable. To address these challenges, we introduce uncertainty-aware t-SNE (Ut-SNE), a noise-defending visualization tool tailored for uncertain single-cell RNA-seq data. By creating a probabilistic representation for each sample, Our Ut-SNE accurately incorporates noise about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data, revealing significant uncertainties in transcriptomic variability. Through various examples, we showcase the practical value of Ut-SNE and underscore the significance of incorporating uncertainty awareness into data visualization practices. This versatile uncertainty-aware visualization tool can be easily adapted to other scientific domains beyond single-cell RNA sequencing, making them valuable resources for high-dimensional data analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.00473v1-abstract-full').style.display = 'none'; document.getElementById('2410.00473v1-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 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/2408.09344">arXiv:2408.09344</a> <span> [<a href="https://arxiv.org/pdf/2408.09344">pdf</a>, <a href="https://arxiv.org/format/2408.09344">other</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> <p class="title is-5 mathjax"> GitHub is an effective platform for collaborative and reproducible laboratory research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+Y">Katharine Y. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Toro-Moreno%2C+M">Maria Toro-Moreno</a>, <a href="/search/q-bio?searchtype=author&query=Subramaniam%2C+A+R">Arvind Rasi Subramaniam</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="2408.09344v2-abstract-short" style="display: inline;"> Laboratory research is a complex, collaborative process that involves several stages, including hypothesis formulation, experimental design, data generation and analysis, and manuscript writing. Although reproducibility and data sharing are increasingly prioritized at the publication stage, integrating these principles at earlier stages of laboratory research has been hampered by the lack of broad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09344v2-abstract-full').style.display = 'inline'; document.getElementById('2408.09344v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.09344v2-abstract-full" style="display: none;"> Laboratory research is a complex, collaborative process that involves several stages, including hypothesis formulation, experimental design, data generation and analysis, and manuscript writing. Although reproducibility and data sharing are increasingly prioritized at the publication stage, integrating these principles at earlier stages of laboratory research has been hampered by the lack of broadly applicable solutions. Here, we propose that the workflow used in modern software development offers a robust framework for enhancing reproducibility and collaboration in laboratory research. In particular, we show that GitHub, a platform widely used for collaborative software projects, can be effectively adapted to organize and document all aspects of a research project's lifecycle in a molecular biology laboratory. We outline a three-step approach for incorporating the GitHub ecosystem into laboratory research workflows: 1. designing and organizing experiments using issues and project boards, 2. documenting experiments and data analyses with a version control system, and 3. ensuring reproducible software environments for data analyses and writing tasks with containerized packages. The versatility, scalability, and affordability of this approach make it suitable for various scenarios, ranging from small research groups to large, cross-institutional collaborations. Adopting this framework from a project's outset can increase the efficiency and fidelity of knowledge transfer within and across research laboratories. An example GitHub repository based on this approach is available at https://github.com/rasilab/github_demo and a template repository that can be copied is available at https://github.com/rasilab/github_template. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09344v2-abstract-full').style.display = 'none'; document.getElementById('2408.09344v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 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">13 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/2408.06563">arXiv:2408.06563</a> <span> [<a href="https://arxiv.org/pdf/2408.06563">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"> Insights, opportunities and challenges provided by large cell atlases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Hemberg%2C+M">Martin Hemberg</a>, <a href="/search/q-bio?searchtype=author&query=Marini%2C+F">Federico Marini</a>, <a href="/search/q-bio?searchtype=author&query=Ghazanfar%2C+S">Shila Ghazanfar</a>, <a href="/search/q-bio?searchtype=author&query=Ajami%2C+A+A">Ahmad Al Ajami</a>, <a href="/search/q-bio?searchtype=author&query=Abassi%2C+N">Najla Abassi</a>, <a href="/search/q-bio?searchtype=author&query=Anchang%2C+B">Benedict Anchang</a>, <a href="/search/q-bio?searchtype=author&query=Benayoun%2C+B+A">B茅r茅nice A. Benayoun</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+Y">Yue Cao</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Ken Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cuesta-Astroz%2C+Y">Yesid Cuesta-Astroz</a>, <a href="/search/q-bio?searchtype=author&query=DeBruine%2C+Z">Zach DeBruine</a>, <a href="/search/q-bio?searchtype=author&query=Dendrou%2C+C+A">Calliope A. Dendrou</a>, <a href="/search/q-bio?searchtype=author&query=De+Vlaminck%2C+I">Iwijn De Vlaminck</a>, <a href="/search/q-bio?searchtype=author&query=Imkeller%2C+K">Katharina Imkeller</a>, <a href="/search/q-bio?searchtype=author&query=Korsunsky%2C+I">Ilya Korsunsky</a>, <a href="/search/q-bio?searchtype=author&query=Lederer%2C+A+R">Alex R. Lederer</a>, <a href="/search/q-bio?searchtype=author&query=Meysman%2C+P">Pieter Meysman</a>, <a href="/search/q-bio?searchtype=author&query=Miller%2C+C">Clint Miller</a>, <a href="/search/q-bio?searchtype=author&query=Mullan%2C+K">Kerry Mullan</a>, <a href="/search/q-bio?searchtype=author&query=Ohler%2C+U">Uwe Ohler</a>, <a href="/search/q-bio?searchtype=author&query=Patikas%2C+N">Nikolaos Patikas</a>, <a href="/search/q-bio?searchtype=author&query=Schuck%2C+J">Jonas Schuck</a>, <a href="/search/q-bio?searchtype=author&query=Siu%2C+J+H">Jacqueline HY Siu</a>, <a href="/search/q-bio?searchtype=author&query=Triche%2C+T+J">Timothy J. Triche Jr.</a>, <a href="/search/q-bio?searchtype=author&query=Tsankov%2C+A">Alex Tsankov</a> , et al. (5 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="2408.06563v1-abstract-short" style="display: inline;"> The field of single-cell biology is growing rapidly and is generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO, allow researchers to access large volumes of curated datasets. Although the majority of the data is from scRNAseq experiments, a wide range of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06563v1-abstract-full').style.display = 'inline'; document.getElementById('2408.06563v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.06563v1-abstract-full" style="display: none;"> The field of single-cell biology is growing rapidly and is generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO, allow researchers to access large volumes of curated datasets. Although the majority of the data is from scRNAseq experiments, a wide range of other modalities are represented as well. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse, and for extracting novel biological insights. Here, we highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06563v1-abstract-full').style.display = 'none'; document.getElementById('2408.06563v1-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> 12 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/2406.14842">arXiv:2406.14842</a> <span> [<a href="https://arxiv.org/pdf/2406.14842">pdf</a>, <a href="https://arxiv.org/format/2406.14842">other</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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Online t-SNE for single-cell RNA-seq </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Hui Ma</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kai 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="2406.14842v1-abstract-short" style="display: inline;"> Due to the sequential sample arrival, changing experiment conditions, and evolution of knowledge, the demand to continually visualize evolving structures of sequential and diverse single-cell RNA-sequencing (scRNA-seq) data becomes indispensable. However, as one of the state-of-the-art visualization and analysis methods for scRNA-seq, t-distributed stochastic neighbor embedding (t-SNE) merely visu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14842v1-abstract-full').style.display = 'inline'; document.getElementById('2406.14842v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14842v1-abstract-full" style="display: none;"> Due to the sequential sample arrival, changing experiment conditions, and evolution of knowledge, the demand to continually visualize evolving structures of sequential and diverse single-cell RNA-sequencing (scRNA-seq) data becomes indispensable. However, as one of the state-of-the-art visualization and analysis methods for scRNA-seq, t-distributed stochastic neighbor embedding (t-SNE) merely visualizes static scRNA-seq data offline and fails to meet the demand well. To address these challenges, we introduce online t-SNE to seamlessly integrate sequential scRNA-seq data. Online t-SNE achieves this by leveraging the embedding space of old samples, exploring the embedding space of new samples, and aligning the two embedding spaces on the fly. Consequently, online t-SNE dramatically enables the continual discovery of new structures and high-quality visualization of new scRNA-seq data without retraining from scratch. We showcase the formidable visualization capabilities of online t-SNE across diverse sequential scRNA-seq datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14842v1-abstract-full').style.display = 'none'; document.getElementById('2406.14842v1-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">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/2405.15206">arXiv:2405.15206</a> <span> [<a href="https://arxiv.org/pdf/2405.15206">pdf</a>, <a href="https://arxiv.org/format/2405.15206">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="Biological Physics">physics.bio-ph</span> </div> </div> <p class="title is-5 mathjax"> Maximum Caliber Infers Effective Coupling and Response from Spiking Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+S">Kevin S. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Y">Ying-Jen 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="2405.15206v1-abstract-short" style="display: inline;"> The characterization of network and biophysical properties from neural spiking activity is an important goal in neuroscience. A framework that provides unbiased inference on causal synaptic interaction and single neural properties has been missing. Here we applied the stochastic dynamics extension of Maximum Entropy -- the Maximum Caliber Principle -- to infer the transition rates of network state… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.15206v1-abstract-full').style.display = 'inline'; document.getElementById('2405.15206v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.15206v1-abstract-full" style="display: none;"> The characterization of network and biophysical properties from neural spiking activity is an important goal in neuroscience. A framework that provides unbiased inference on causal synaptic interaction and single neural properties has been missing. Here we applied the stochastic dynamics extension of Maximum Entropy -- the Maximum Caliber Principle -- to infer the transition rates of network states. Effective synaptic coupling strength and neuronal response functions for various network motifs can then be computed. The inferred minimal model also enables leading-order reconstruction of inter-spike interval distribution. Our method is tested with numerical simulated spiking networks and applied to data from salamander retina. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.15206v1-abstract-full').style.display = 'none'; document.getElementById('2405.15206v1-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.12993">arXiv:2402.12993</a> <span> [<a href="https://arxiv.org/pdf/2402.12993">pdf</a>, <a href="https://arxiv.org/format/2402.12993">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> An Autonomous Large Language Model Agent for Chemical Literature Data Mining </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kexin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+H">Hanqun Cao</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+J">Junyou Li</a>, <a href="/search/q-bio?searchtype=author&query=Du%2C+Y">Yuyang Du</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+M">Menghao Guo</a>, <a href="/search/q-bio?searchtype=author&query=Zeng%2C+X">Xin Zeng</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+L">Lanqing Li</a>, <a href="/search/q-bio?searchtype=author&query=Qiu%2C+J">Jiezhong Qiu</a>, <a href="/search/q-bio?searchtype=author&query=Heng%2C+P+A">Pheng Ann Heng</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Guangyong 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="2402.12993v1-abstract-short" style="display: inline;"> Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging researchers to discern patterns and refine synthesis processes. Artificial intelligence (AI) helps by analyzing data to optimize synthesis and increase… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.12993v1-abstract-full').style.display = 'inline'; document.getElementById('2402.12993v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.12993v1-abstract-full" style="display: none;"> Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging researchers to discern patterns and refine synthesis processes. Artificial intelligence (AI) helps by analyzing data to optimize synthesis and increase yields. However, AI faces challenges in processing literature data due to the unstructured format and diverse writing style of chemical literature. To overcome these difficulties, we introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. This AI agent employs large language models (LLMs) for prompt generation and iterative optimization. It functions as a chemistry assistant, automating data collection and analysis, thereby saving manpower and enhancing performance. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data, and we compared our method with human experts in terms of content correctness and time efficiency. The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.12993v1-abstract-full').style.display = 'none'; document.getElementById('2402.12993v1-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 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/2311.07117">arXiv:2311.07117</a> <span> [<a href="https://arxiv.org/pdf/2311.07117">pdf</a>, <a href="https://arxiv.org/format/2311.07117">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="Biological Physics">physics.bio-ph</span> </div> </div> <p class="title is-5 mathjax"> Olfactory learning alters navigation strategies and behavioral variability in C. elegans </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+S">Kevin S. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Sharma%2C+A+K">Anuj K. Sharma</a>, <a href="/search/q-bio?searchtype=author&query=Pillow%2C+J+W">Jonathan W. Pillow</a>, <a href="/search/q-bio?searchtype=author&query=Leifer%2C+A+M">Andrew M. Leifer</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="2311.07117v2-abstract-short" style="display: inline;"> Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation behavior depending on the association of odor butanone with food (appetitive training) or starvation (aversive training), and will then climb up the butan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07117v2-abstract-full').style.display = 'inline'; document.getElementById('2311.07117v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.07117v2-abstract-full" style="display: none;"> Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation behavior depending on the association of odor butanone with food (appetitive training) or starvation (aversive training), and will then climb up the butanone gradient or ignore it, respectively. However, the exact change in navigation strategy in response to learning is still unknown. Here we study the learned odor navigation in worms by combining precise experimental measurement and a novel descriptive model of navigation. Our model consists of two known navigation strategies in worms: biased random walk and weathervaning. We infer weights on these strategies by applying the model to worm navigation trajectories and the exact odor concentration it experiences. Compared to naive worms, appetitive trained worms up-regulate the biased random walk strategy, and aversive trained worms down-regulate the weathervaning strategy. The statistical model provides prediction with $>90 \%$ accuracy of the past training condition given navigation data, which outperforms the classical chemotaxis metric. We find that the behavioral variability is altered by learning, such that worms are less variable after training compared to naive ones. The model further predicts the learning-dependent response and variability under optogenetic perturbation of the olfactory neuron AWC$^\mathrm{ON}$. Lastly, we investigate neural circuits downstream from AWC$^\mathrm{ON}$ that are differentially recruited for learned odor-guided navigation. Together, we provide a new paradigm to quantify flexible navigation algorithms and pinpoint the underlying neural substrates. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07117v2-abstract-full').style.display = 'none'; document.getElementById('2311.07117v2-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">v1</span> submitted 13 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/2311.00567">arXiv:2311.00567</a> <span> [<a href="https://arxiv.org/pdf/2311.00567">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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yao%2C+N">Ni Yao</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+H">Hang Hu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kaicong Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+C">Chen Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+Y">Yuan Guo</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+B">Boya Li</a>, <a href="/search/q-bio?searchtype=author&query=Nan%2C+J">Jiaofen Nan</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yanting Li</a>, <a href="/search/q-bio?searchtype=author&query=Han%2C+C">Chuang Han</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+F">Fubao Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+W">Weihua Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Tian%2C+L">Li Tian</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="2311.00567v2-abstract-short" style="display: inline;"> Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients, pathologically proven RCC, were retrospectively collected from Center 1. By using five-fold cross… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00567v2-abstract-full').style.display = 'inline'; document.getElementById('2311.00567v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.00567v2-abstract-full" style="display: none;"> Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients, pathologically proven RCC, were retrospectively collected from Center 1. By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external validation set of 78 patients from Center 2 further evaluated the model's performance. Results In the five-fold cross-validation, the model's area under the receiver operating characteristic curve (AUC) for the classification of ccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI: 0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the external validation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI: 0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC, respectively. Conclusions The developed deep learning model demonstrated robust performance in predicting the pathological subtypes of RCC, while the incorporated uncertainty emphasized the importance of understanding model confidence, which is crucial for assisting clinical decision-making for patients with renal tumors. Clinical relevance statement Our deep learning approach, integrated with uncertainty estimation, offers clinicians a dual advantage: accurate RCC subtype predictions complemented by diagnostic confidence references, promoting informed decision-making for patients with RCC. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00567v2-abstract-full').style.display = 'none'; document.getElementById('2311.00567v2-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> 12 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 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">Comments:</span> <span class="has-text-grey-dark mathjax">16 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/2309.16498">arXiv:2309.16498</a> <span> [<a href="https://arxiv.org/pdf/2309.16498">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</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"> Coordinates in low-dimensional cell shape-space discriminate migration dynamics from single static cell images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=He%2C+X">Xiuxiu He</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kuangcai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Fang%2C+N">Ning Fang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</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="2309.16498v1-abstract-short" style="display: inline;"> Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16498v1-abstract-full').style.display = 'inline'; document.getElementById('2309.16498v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.16498v1-abstract-full" style="display: none;"> Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation space has only six dimensions, and migration behavior can be determined by the coordinates of a single cell image in this 6-dimensional shape-space. We further show that this is possible because persistent cell migration is characterized by spatial-temporally coordinated protrusion and contraction, and a distribution signature in the shape-space. Our findings provide a quantitative underpinning for using cell morphology to differentiate cell dynamical behavior. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16498v1-abstract-full').style.display = 'none'; document.getElementById('2309.16498v1-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> 28 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">29 pages, 9 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/2308.02995">arXiv:2308.02995</a> <span> [<a href="https://arxiv.org/pdf/2308.02995">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"> mSigSDK -- private, at scale, computation of mutation signatures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ge%2C+A">Aaron Ge</a>, <a href="/search/q-bio?searchtype=author&query=Martins%2C+Y+C">Yasmmin C么rtes Martins</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+T">Tongwu Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kailing Chen</a>, <a href="/search/q-bio?searchtype=author&query=Landi%2C+M+T">Maria Teresa Landi</a>, <a href="/search/q-bio?searchtype=author&query=Park%2C+B">Brian Park</a>, <a href="/search/q-bio?searchtype=author&query=Balasubramanian%2C+J">Jeya Balasubramanian</a>, <a href="/search/q-bio?searchtype=author&query=Almeida%2C+J+S">Jonas S Almeida</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="2308.02995v2-abstract-short" style="display: inline;"> In our previous work, we demonstrated that it is feasible to perform analysis on mutation signature data without the need for downloads or installations and analyze individual patient data at scale without compromising privacy. Building on this foundation, we developed a Software Development Kit (SDK) called mSigSDK to facilitate the orchestration of distributed data processing workflows and graph… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02995v2-abstract-full').style.display = 'inline'; document.getElementById('2308.02995v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.02995v2-abstract-full" style="display: none;"> In our previous work, we demonstrated that it is feasible to perform analysis on mutation signature data without the need for downloads or installations and analyze individual patient data at scale without compromising privacy. Building on this foundation, we developed a Software Development Kit (SDK) called mSigSDK to facilitate the orchestration of distributed data processing workflows and graphic visualization of mutational signature analysis results. We strictly adhered to modern web computing standards, particularly the modularization standards set by the ECMAScript ES6 framework (JavaScript modules). Our approach allows for computation to be entirely performed by secure delegation to the computational resources of the user's own machine (in-browser), without any downloads or installations. The mSigSDK was developed primarily as a companion library to the mSig Portal resource of the National Cancer Institute Division of Cancer Epidemiology and Genetics (NIH/NCI/DCEG), with a focus on its FAIR extensibility as components of other researchers' computational constructs. Anticipated extensions include the programmatic operation of other mutation signature API ecosystems such as SIGNAL and COSMIC, advancing towards a data commons for mutational signature research (Grossman et al., 2016). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02995v2-abstract-full').style.display = 'none'; document.getElementById('2308.02995v2-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 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/2301.05905">arXiv:2301.05905</a> <span> [<a href="https://arxiv.org/pdf/2301.05905">pdf</a>, <a href="https://arxiv.org/format/2301.05905">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"> Continuous odor profile monitoring to study olfactory navigation in small animals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+S">Kevin S. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+R">Rui Wu</a>, <a href="/search/q-bio?searchtype=author&query=Gershow%2C+M+H">Marc H. Gershow</a>, <a href="/search/q-bio?searchtype=author&query=Leifer%2C+A+M">Andrew M. Leifer</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="2301.05905v1-abstract-short" style="display: inline;"> Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal's sensory environment. For small model organisms like C. elegans and larval D. melanogaster, controlling and measuring the odor environment ex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.05905v1-abstract-full').style.display = 'inline'; document.getElementById('2301.05905v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.05905v1-abstract-full" style="display: none;"> Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal's sensory environment. For small model organisms like C. elegans and larval D. melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor's adhesion, adsorption or reemission. Here we present a method to flexibly control and precisely measure airborne odor concentration in an arena with agar while imaging animal behavior. Crucially and unlike previous methods, our method allows continuous monitoring of the odor profile during behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be accurately inferred and continuously monitored at all points in time. We use this approach to measure the precise odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies for C. elegans and D. melanogaster larvae populations under different spatial odor landscapes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.05905v1-abstract-full').style.display = 'none'; document.getElementById('2301.05905v1-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 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.10083">arXiv:2208.10083</a> <span> [<a href="https://arxiv.org/pdf/2208.10083">pdf</a>, <a href="https://arxiv.org/format/2208.10083">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"> MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kexin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Guangyong Chen</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+J">Junyou Li</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Yuansheng Huang</a>, <a href="/search/q-bio?searchtype=author&query=Heng%2C+P">Pheng-Ann Heng</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="2208.10083v1-abstract-short" style="display: inline;"> Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting hig… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.10083v1-abstract-full').style.display = 'inline'; document.getElementById('2208.10083v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.10083v1-abstract-full" style="display: none;"> Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based differentiable random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.10083v1-abstract-full').style.display = 'none'; document.getElementById('2208.10083v1-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.05242">arXiv:2205.05242</a> <span> [<a href="https://arxiv.org/pdf/2205.05242">pdf</a>, <a href="https://arxiv.org/format/2205.05242">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</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"> Principal Amalgamation Analysis for Microbiome Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yan Li</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+G">Gen Li</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kun 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="2205.05242v1-abstract-short" style="display: inline;"> In recent years microbiome studies have become increasingly prevalent and large-scale. Through high-throughput sequencing technologies and well-established analytical pipelines, relative abundance data of operational taxonomic units and their associated taxonomic structures are routinely produced. Since such data can be extremely sparse and high dimensional, there is often a genuine need for dimen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.05242v1-abstract-full').style.display = 'inline'; document.getElementById('2205.05242v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.05242v1-abstract-full" style="display: none;"> In recent years microbiome studies have become increasingly prevalent and large-scale. Through high-throughput sequencing technologies and well-established analytical pipelines, relative abundance data of operational taxonomic units and their associated taxonomic structures are routinely produced. Since such data can be extremely sparse and high dimensional, there is often a genuine need for dimension reduction to facilitate data visualization and downstream statistical analysis. We propose Principal Amalgamation Analysis (PAA), a novel amalgamation-based and taxonomy-guided dimension reduction paradigm for microbiome data. Our approach aims to aggregate the compositions into a smaller number of principal compositions, guided by the available taxonomic structure, by minimizing a properly measured loss of information. The choice of the loss function is flexible and can be based on familiar diversity indices for preserving either within-sample or between-sample diversity in the data. To enable scalable computation, we develop a hierarchical PAA algorithm to trace the entire trajectory of successive simple amalgamations. Visualization tools including dendrogram, scree plot, and ordination plot are developed. The effectiveness of PAA is demonstrated using gut microbiome data from a preterm infant study and an HIV infection study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.05242v1-abstract-full').style.display = 'none'; document.getElementById('2205.05242v1-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.12595">arXiv:2204.12595</a> <span> [<a href="https://arxiv.org/pdf/2204.12595">pdf</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 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/10.1371/journal.pcbi.1010421">10.1371/journal.pcbi.1010421 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Correcting motion induced fluorescence artifacts in two-channel neural imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Creamer%2C+M+S">Matthew S. Creamer</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+S">Kevin S. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Leifer%2C+A+M">Andrew M. Leifer</a>, <a href="/search/q-bio?searchtype=author&query=Pillow%2C+J+W">Jonathan W. Pillow</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="2204.12595v1-abstract-short" style="display: inline;"> Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels; one that captures an activity-dependent fluorophore, such as GCaMP, and another that ca… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.12595v1-abstract-full').style.display = 'inline'; document.getElementById('2204.12595v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.12595v1-abstract-full" style="display: none;"> Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels; one that captures an activity-dependent fluorophore, such as GCaMP, and another that captures an activity-independent fluorophore such as RFP. Because the activity-independent channel contains the same motion artifacts as the activity-dependent channel, but no neural signals, the two together can be used to remove the artifacts. Existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel independent noise in the measured fluorescence. Moreover, no systematic comparison has been made of existing approaches that use two-channel signals. Here, we present Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove artifacts by specifying a generative model of the fluorescence of the two channels as a function of motion artifact, neural activity, and noise. We further present a novel method for evaluating ground-truth performance of motion correction algorithms by comparing the decodability of behavior from two types of neural recordings; a recording that had both an activity-dependent fluorophore (GCaMP and RFP) and a recording where both fluorophores were activity-independent (GFP and RFP). A successful motion-correction method should decode behavior from the first type of recording, but not the second. We use this metric to systematically compare five methods for removing motion artifacts from fluorescent time traces. We decode locomotion from a GCaMP expressing animal 15x more accurately on average than from control when using TMAC inferred activity and outperform all other methods of motion correction tested. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.12595v1-abstract-full').style.display = 'none'; document.getElementById('2204.12595v1-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 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">11 pages, 3 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/2202.10919">arXiv:2202.10919</a> <span>  </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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Nguyen%2C+N">Nam Nguyen</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kwang-Cheng 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="2202.10919v3-abstract-short" style="display: inline;"> Quantifying the dynamics of tumor burden reveals useful information about cancer evolution concerning treatment effects and drug resistance, which play a crucial role in advancing model-informed drug developments (MIDD) towards personalized medicine and precision oncology. The emergence of Quantum Machine Intelligence offers unparalleled insights into tumor dynamics via a quantum mechanics perspec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.10919v3-abstract-full').style.display = 'inline'; document.getElementById('2202.10919v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.10919v3-abstract-full" style="display: none;"> Quantifying the dynamics of tumor burden reveals useful information about cancer evolution concerning treatment effects and drug resistance, which play a crucial role in advancing model-informed drug developments (MIDD) towards personalized medicine and precision oncology. The emergence of Quantum Machine Intelligence offers unparalleled insights into tumor dynamics via a quantum mechanics perspective. This paper introduces a novel hybrid quantum-classical neural architecture named $畏-$Net that enables quantifying quantum dynamics of tumor burden concerning treatment effects. We evaluate our proposed neural solution on two major use cases, including cohort-specific and patient-specific modeling. In silico numerical results show a high capacity and expressivity of $畏-$Net to the quantified biological problem. Moreover, the close connection to representation learning - the foundation for successes of modern AI, enables efficient transferability of empirical knowledge from relevant cohorts to targeted patients. Finally, we leverage Bayesian optimization to quantify the epistemic uncertainty of model predictions, paving the way for $畏-$Net towards reliable AI in decision-making for clinical usages. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.10919v3-abstract-full').style.display = 'none'; document.getElementById('2202.10919v3-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> 7 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">Withdraw because of error. The error is RY rotation fomular</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.09521">arXiv:2110.09521</a> <span> [<a href="https://arxiv.org/pdf/2110.09521">pdf</a>, <a href="https://arxiv.org/format/2110.09521">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"> Quantitative relations among causality measures with applications to nonlinear pulse-output network reconstruction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tian%2C+Z+K">Zhong-qi K. Tian</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+S">Songting Li</a>, <a href="/search/q-bio?searchtype=author&query=McLaughlin%2C+D+W">David W. McLaughlin</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+D">Douglas 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="2110.09521v1-abstract-short" style="display: inline;"> The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.09521v1-abstract-full').style.display = 'inline'; document.getElementById('2110.09521v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.09521v1-abstract-full" style="display: none;"> The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, $e.g.$, neural networks with spike output, and address the above issues based on four intensively utilized causality measures, $i.e.$, time-delayed correlation, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking the simulated Hodgkin-Huxley neural network and the real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore establishing a direct link between the causal and structural connectivity. We stress that the structural connectivity of networks can be reconstructed pairwisely without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.09521v1-abstract-full').style.display = 'none'; document.getElementById('2110.09521v1-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.06610">arXiv:2108.06610</a> <span> [<a href="https://arxiv.org/pdf/2108.06610">pdf</a>, <a href="https://arxiv.org/format/2108.06610">other</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"> SquiggleFilter: An Accelerator for Portable Virus Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Dunn%2C+T">Tim Dunn</a>, <a href="/search/q-bio?searchtype=author&query=Sadasivan%2C+H">Harisankar Sadasivan</a>, <a href="/search/q-bio?searchtype=author&query=Wadden%2C+J">Jack Wadden</a>, <a href="/search/q-bio?searchtype=author&query=Goliya%2C+K">Kush Goliya</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kuan-Yu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Das%2C+R">Reetuparna Das</a>, <a href="/search/q-bio?searchtype=author&query=Blaauw%2C+D">David Blaauw</a>, <a href="/search/q-bio?searchtype=author&query=Narayanasamy%2C+S">Satish Narayanasamy</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="2108.06610v2-abstract-short" style="display: inline;"> The MinION is a recent-to-market handheld nanopore sequencer. It can be used to determine the whole genome of a target virus in a biological sample. Its Read Until feature allows us to skip sequencing a majority of non-target reads (DNA/RNA fragments), which constitutes more than 99% of all reads in a typical sample. However, it does not have any on-board computing, which significantly limits its… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.06610v2-abstract-full').style.display = 'inline'; document.getElementById('2108.06610v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.06610v2-abstract-full" style="display: none;"> The MinION is a recent-to-market handheld nanopore sequencer. It can be used to determine the whole genome of a target virus in a biological sample. Its Read Until feature allows us to skip sequencing a majority of non-target reads (DNA/RNA fragments), which constitutes more than 99% of all reads in a typical sample. However, it does not have any on-board computing, which significantly limits its portability. We analyze the performance of a Read Until metagenomic pipeline for detecting target viruses and identifying strain-specific mutations. We find new sources of performance bottlenecks (basecaller in classification of a read) that are not addressed by past genomics accelerators. We present SquiggleFilter, a novel hardware accelerated dynamic time warping (DTW) based filter that directly analyzes MinION's raw squiggles and filters everything except target viral reads, thereby avoiding the expensive basecalling step. We show that our 14.3W 13.25mm2 accelerator has 274X greater throughput and 3481X lower latency than existing GPU-based solutions while consuming half the power, enabling Read Until for the next generation of nanopore sequencers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.06610v2-abstract-full').style.display = 'none'; document.getElementById('2108.06610v2-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 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">https://micro2021ae.hotcrp.com/paper/12?cap=012aOJj-0U08_9o</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.13252">arXiv:2012.13252</a> <span> [<a href="https://arxiv.org/pdf/2012.13252">pdf</a>, <a href="https://arxiv.org/format/2012.13252">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="Biological Physics">physics.bio-ph</span> </div> </div> <p class="title is-5 mathjax"> Nonequilibrium thermodynamics of input-driven networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+S">Kevin 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="2012.13252v1-abstract-short" style="display: inline;"> Neural dynamics of energy-based models are governed by energy minimization and the patterns stored in the network are retrieved when the system reaches equilibrium. However, when the system is driven by time-varying external input, the nonequilibrium process of such physical system has not been well characterized. Here, we study attractor neural networks, specifically the Hopfield network, driven… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.13252v1-abstract-full').style.display = 'inline'; document.getElementById('2012.13252v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.13252v1-abstract-full" style="display: none;"> Neural dynamics of energy-based models are governed by energy minimization and the patterns stored in the network are retrieved when the system reaches equilibrium. However, when the system is driven by time-varying external input, the nonequilibrium process of such physical system has not been well characterized. Here, we study attractor neural networks, specifically the Hopfield network, driven by time-varying external input and measure thermodynamic quantities along trajectories between two collective states. The overlap between distribution of the forward and reversal work along the nonequilibrium trajectories agrees with the equilibrium free energy difference between two states, following the prediction of Crooks fluctuation theorem. We study conditions with different stimulation protocol and neural network constraints. We further discuss how biologically plausible synaptic connections and information processing may play a role in this nonequilibrium framework. These results demonstrate how nonequilibrium thermodynamics can be relevant for neural computation and connect to recent systems neuroscience studies with closed-loop dynamic perturbations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.13252v1-abstract-full').style.display = 'none'; document.getElementById('2012.13252v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </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">8 pages, 7 figures, first draft</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.05469">arXiv:2005.05469</a> <span> [<a href="https://arxiv.org/pdf/2005.05469">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Economics">econ.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+M+K">M. Keith Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhuo%2C+Y">Yilin Zhuo</a>, <a href="/search/q-bio?searchtype=author&query=de+la+Fuente%2C+M">Malena de la Fuente</a>, <a href="/search/q-bio?searchtype=author&query=Rohla%2C+R">Ryne Rohla</a>, <a href="/search/q-bio?searchtype=author&query=Long%2C+E+F">Elisa F. Long</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="2005.05469v1-abstract-short" style="display: inline;"> Accurately estimating the effectiveness of stay-at-home orders (SHOs) on reducing social contact and disease spread is crucial for mitigating pandemics. Leveraging individual-level location data for 10 million smartphones, we observe that by April 30th---when nine in ten Americans were under a SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this decline is causally attrib… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.05469v1-abstract-full').style.display = 'inline'; document.getElementById('2005.05469v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.05469v1-abstract-full" style="display: none;"> Accurately estimating the effectiveness of stay-at-home orders (SHOs) on reducing social contact and disease spread is crucial for mitigating pandemics. Leveraging individual-level location data for 10 million smartphones, we observe that by April 30th---when nine in ten Americans were under a SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this decline is causally attributable to SHOs, with wide demographic differences in compliance, most notably by political affiliation. Likely Trump voters reduce movement by 9% following a local SHO, compared to a 21% reduction among their Clinton-voting neighbors, who face similar exposure risks and identical government orders. Linking social distancing behavior with an epidemic model, we estimate that reductions in movement have causally reduced SARS-CoV-2 transmission rates by 49%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.05469v1-abstract-full').style.display = 'none'; document.getElementById('2005.05469v1-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 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.05003">arXiv:2003.05003</a> <span> [<a href="https://arxiv.org/pdf/2003.05003">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 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/10.1136/bmjopen-2020-043863">10.1136/bmjopen-2020-043863 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Jingyuan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+K">Ke Tang</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+K">Kai Feng</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xin Li</a>, <a href="/search/q-bio?searchtype=author&query=Lv%2C+W">Weifeng Lv</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kun Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+F">Fei 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="2003.05003v5-abstract-short" style="display: inline;"> Objectives: We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design: A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting: We use the data for COVID-19 daily symptom-o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.05003v5-abstract-full').style.display = 'inline'; document.getElementById('2003.05003v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.05003v5-abstract-full" style="display: none;"> Objectives: We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design: A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting: We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants: A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results: Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions: Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.05003v5-abstract-full').style.display = 'none'; document.getElementById('2003.05003v5-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> 30 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> BMJ Open 2021;11:e043863 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.03283">arXiv:1909.03283</a> <span> [<a href="https://arxiv.org/pdf/1909.03283">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</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/10.1021/jacs.9b06949">10.1021/jacs.9b06949 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Enhanced diffusion and enzyme dissociation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jee%2C+A">Ah-Young Jee</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kuo Chen</a>, <a href="/search/q-bio?searchtype=author&query=Tlusty%2C+T">Tsvi Tlusty</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+J">Jiang Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Granick%2C+S">Steve Granick</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="1909.03283v2-abstract-short" style="display: inline;"> The concept that catalytic enzymes can act as molecular machines transducing chemical activity into motion has conceptual and experimental support, but much of the claimed support comes from experimental conditions where the substrate concentration is higher than biologically relevant and accordingly exceeds kM, the Michaelis-Menten constant. Moreover, many of the enzymes studied experimentally to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.03283v2-abstract-full').style.display = 'inline'; document.getElementById('1909.03283v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.03283v2-abstract-full" style="display: none;"> The concept that catalytic enzymes can act as molecular machines transducing chemical activity into motion has conceptual and experimental support, but much of the claimed support comes from experimental conditions where the substrate concentration is higher than biologically relevant and accordingly exceeds kM, the Michaelis-Menten constant. Moreover, many of the enzymes studied experimentally to date are oligomeric. Urease, a hexamer of subunits, has been considered to be the gold standard demonstrating enhanced diffusion. Here we show that urease and certain other oligomeric enzymes of high catalytic activity above kM dissociate into their smaller subunit fragments that diffuse more rapidly, thus providing a simple physical mechanism of enhanced diffusion in this regime of concentrations. Mindful that this conclusion may be controversial, our findings are sup-ported by four independent analytical techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chroma-tography (SEC), and fluorescence correlation spectroscopy (FCS). Data for urease are presented in the main text and the con-clusion is validated for hexokinase and acetylcholinesterase with data presented in supplementary information. For substrate concentration regimes below kM at which these enzymes do not dissociate, our findings from both FCS and DLS validate that enzymatic catalysis does lead to the enhanced diffusion phenomenon. INTRODUCT <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.03283v2-abstract-full').style.display = 'none'; document.getElementById('1909.03283v2-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> 30 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">7 pages, 5 figures, supplementary information</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Journal of the American Chemical Society 2019 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.01651">arXiv:1901.01651</a> <span> [<a href="https://arxiv.org/pdf/1901.01651">pdf</a>, <a href="https://arxiv.org/format/1901.01651">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="Computational Geometry">cs.CG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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.patcog.2019.107064">10.1016/j.patcog.2019.107064 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Tooth morphometry using quasi-conformal theory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Choi%2C+G+P+T">Gary P. T. Choi</a>, <a href="/search/q-bio?searchtype=author&query=Chan%2C+H+L">Hei Long Chan</a>, <a href="/search/q-bio?searchtype=author&query=Yong%2C+R">Robin Yong</a>, <a href="/search/q-bio?searchtype=author&query=Ranjitkar%2C+S">Sarbin Ranjitkar</a>, <a href="/search/q-bio?searchtype=author&query=Brook%2C+A">Alan Brook</a>, <a href="/search/q-bio?searchtype=author&query=Townsend%2C+G">Grant Townsend</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Ke Chen</a>, <a href="/search/q-bio?searchtype=author&query=Lui%2C+L+M">Lok Ming Lui</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="1901.01651v1-abstract-short" style="display: inline;"> Shape analysis is important in anthropology, bioarchaeology and forensic science for interpreting useful information from human remains. In particular, teeth are morphologically stable and hence well-suited for shape analysis. In this work, we propose a framework for tooth morphometry using quasi-conformal theory. Landmark-matching Teichm眉ller maps are used for establishing a 1-1 correspondence be… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.01651v1-abstract-full').style.display = 'inline'; document.getElementById('1901.01651v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.01651v1-abstract-full" style="display: none;"> Shape analysis is important in anthropology, bioarchaeology and forensic science for interpreting useful information from human remains. In particular, teeth are morphologically stable and hence well-suited for shape analysis. In this work, we propose a framework for tooth morphometry using quasi-conformal theory. Landmark-matching Teichm眉ller maps are used for establishing a 1-1 correspondence between tooth surfaces with prescribed anatomical landmarks. Then, a quasi-conformal statistical shape analysis model based on the Teichm眉ller mapping results is proposed for building a tooth classification scheme. We deploy our framework on a dataset of human premolars to analyze the tooth shape variation among genders and ancestries. Experimental results show that our method achieves much higher classification accuracy with respect to both gender and ancestry when compared to the existing methods. Furthermore, our model reveals the underlying tooth shape difference between different genders and ancestries in terms of the local geometric distortion and curvatures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.01651v1-abstract-full').style.display = 'none'; document.getElementById('1901.01651v1-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, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Pattern Recognition 99, 107064 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1809.05619">arXiv:1809.05619</a> <span> [<a href="https://arxiv.org/pdf/1809.05619">pdf</a>, <a href="https://arxiv.org/format/1809.05619">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Subcellular Processes">q-bio.SC</span> </div> </div> <p class="title is-5 mathjax"> Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+Y">Kevin Y. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zuckerman%2C+D+M">Daniel M. Zuckerman</a>, <a href="/search/q-bio?searchtype=author&query=Nelson%2C+P+C">Philip C. Nelson</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="1809.05619v1-abstract-short" style="display: inline;"> Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential-equation approach by generating typical system histories instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch, that is, without recourse to black-box packages. In some cases, the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.05619v1-abstract-full').style.display = 'inline'; document.getElementById('1809.05619v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1809.05619v1-abstract-full" style="display: none;"> Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential-equation approach by generating typical system histories instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch, that is, without recourse to black-box packages. In some cases, their results can also be compared directly to single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies, involving gene expression and error correction, respectively. Code samples and resulting animations showing results are given in the online supplements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.05619v1-abstract-full').style.display = 'none'; document.getElementById('1809.05619v1-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, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.10283">arXiv:1803.10283</a> <span> [<a href="https://arxiv.org/pdf/1803.10283">pdf</a>, <a href="https://arxiv.org/ps/1803.10283">ps</a>, <a href="https://arxiv.org/format/1803.10283">other</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="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</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/10.1063/1.5031010">10.1063/1.5031010 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Promoting single-file DNA translocations through nanopores using electroosmotic flow </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ermann%2C+N">Niklas Ermann</a>, <a href="/search/q-bio?searchtype=author&query=Hanikel%2C+N">Nikita Hanikel</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+V">Vivian Wang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kaikai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Keyser%2C+U+F">Ulrich F. Keyser</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="1803.10283v1-abstract-short" style="display: inline;"> Double-stranded DNA translocates through sufficiently large nanopores either in a linear, single-file fashion or in a folded hairpin conformation when captured somewhere along its length. We show that the folding state of DNA can be controlled by changing the electrolyte concentration, pH and PEG content of the measurement buffer. At 1 M LiCl or 0.35 M KCl in neutral pH, single-file translocations… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.10283v1-abstract-full').style.display = 'inline'; document.getElementById('1803.10283v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.10283v1-abstract-full" style="display: none;"> Double-stranded DNA translocates through sufficiently large nanopores either in a linear, single-file fashion or in a folded hairpin conformation when captured somewhere along its length. We show that the folding state of DNA can be controlled by changing the electrolyte concentration, pH and PEG content of the measurement buffer. At 1 M LiCl or 0.35 M KCl in neutral pH, single-file translocations make up more than 90% of the total. We attribute the effect to the onset of electroosmotic flow from the pore at low ionic strength. Our hypothesis on the critical role of flows is supported by the preferred orientation of entry of a strand that has been folded into a multi-helix structure at one end. Control over DNA folding is critical for nanopore sensing approaches that use modifications along a DNA strand and the associated secondary current drops to encode information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.10283v1-abstract-full').style.display = 'none'; document.getElementById('1803.10283v1-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 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.00962">arXiv:1712.00962</a> <span> [<a href="https://arxiv.org/pdf/1712.00962">pdf</a>, <a href="https://arxiv.org/ps/1712.00962">ps</a>, <a href="https://arxiv.org/format/1712.00962">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</span> </div> </div> <p class="title is-5 mathjax"> A Bayesian statistical analysis of stochastic phenotypic plasticity model of cancer cells </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhou%2C+D">Da Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Mao%2C+S">Shanjun Mao</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kaiyi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+X">Xiaofang Cao</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+J">Jie 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="1712.00962v1-abstract-short" style="display: inline;"> The phenotypic plasticity of cancer cells has received special attention in recent years. Even though related models have been widely studied in terms of mathematical properties, a thorough statistical analysis on parameter estimation and model selection is still very lacking. In this study, we present a Bayesian approach on the relative frequencies of cancer stem cells (CSCs). Both Gibbs sampling… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.00962v1-abstract-full').style.display = 'inline'; document.getElementById('1712.00962v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.00962v1-abstract-full" style="display: none;"> The phenotypic plasticity of cancer cells has received special attention in recent years. Even though related models have been widely studied in terms of mathematical properties, a thorough statistical analysis on parameter estimation and model selection is still very lacking. In this study, we present a Bayesian approach on the relative frequencies of cancer stem cells (CSCs). Both Gibbs sampling and Metropolis-Hastings (MH) algorithm are used to perform point and interval estimations of cell-state transition rates between CSCs and non-CSCs. Extensive simulations demonstrate the validity of our model and algorithm. By applying this method to a published data on SW620 colon cancer cell line, the model selection favors the phenotypic plasticity model, relative to conventional hierarchical model of cancer cells. Moreover, it is found that the initial state of CSCs after cell sorting significantly influences the occurrence of phenotypic plasticity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.00962v1-abstract-full').style.display = 'none'; document.getElementById('1712.00962v1-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 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2017. </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, 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/1611.03441">arXiv:1611.03441</a> <span> [<a href="https://arxiv.org/pdf/1611.03441">pdf</a>, <a href="https://arxiv.org/format/1611.03441">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"> Measurement of Anticipative Power of a Retina by Predictive Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+S">Kevin Sean Chen</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+C">Chun-Chung Chen</a>, <a href="/search/q-bio?searchtype=author&query=Chan%2C+C+K">C. K. Chan</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="1611.03441v1-abstract-short" style="display: inline;"> The predictive properties of a retina are studied by measuring the mutual information (MI) between its stimulation and the corresponding firing rates while it is being probed by a train of short pulses with stochastic intervals. Features of the measured MI at various time shifts between the stimulation and the response are used to characterize the predictive properties of the retina. By varying th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.03441v1-abstract-full').style.display = 'inline'; document.getElementById('1611.03441v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1611.03441v1-abstract-full" style="display: none;"> The predictive properties of a retina are studied by measuring the mutual information (MI) between its stimulation and the corresponding firing rates while it is being probed by a train of short pulses with stochastic intervals. Features of the measured MI at various time shifts between the stimulation and the response are used to characterize the predictive properties of the retina. By varying the statistical properties of the pulse train, our experiments show that a retina has the ability to predict future events of the stimulation if the information rate of the stimulation is low enough. Also, this predictive property of the retina occurs at a time scale similar to the well established anticipative phenomenon of omitted stimulus response in a retina. Furthermore, a retina can make use of its predictive ability to distinguish between time series created by an Ornstein--Uhlenbeck and a hidden Markovian process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.03441v1-abstract-full').style.display = 'none'; document.getElementById('1611.03441v1-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 November, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2016. </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">6 pages, 7 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/1506.01744">arXiv:1506.01744</a> <span> [<a href="https://arxiv.org/pdf/1506.01744">pdf</a>, <a href="https://arxiv.org/format/1506.01744">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">stat.ML</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="Statistics Theory">math.ST</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"> Spectral Learning of Large Structured HMMs for Comparative Epigenomics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhang%2C+C">Chicheng Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+J">Jimin Song</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+C">Kevin C Chen</a>, <a href="/search/q-bio?searchtype=author&query=Chaudhuri%2C+K">Kamalika Chaudhuri</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="1506.01744v1-abstract-short" style="display: inline;"> We develop a latent variable model and an efficient spectral algorithm motivated by the recent emergence of very large data sets of chromatin marks from multiple human cell types. A natural model for chromatin data in one cell type is a Hidden Markov Model (HMM); we model the relationship between multiple cell types by connecting their hidden states by a fixed tree of known structure. The main cha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.01744v1-abstract-full').style.display = 'inline'; document.getElementById('1506.01744v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1506.01744v1-abstract-full" style="display: none;"> We develop a latent variable model and an efficient spectral algorithm motivated by the recent emergence of very large data sets of chromatin marks from multiple human cell types. A natural model for chromatin data in one cell type is a Hidden Markov Model (HMM); we model the relationship between multiple cell types by connecting their hidden states by a fixed tree of known structure. The main challenge with learning parameters of such models is that iterative methods such as EM are very slow, while naive spectral methods result in time and space complexity exponential in the number of cell types. We exploit properties of the tree structure of the hidden states to provide spectral algorithms that are more computationally efficient for current biological datasets. We provide sample complexity bounds for our algorithm and evaluate it experimentally on biological data from nine human cell types. Finally, we show that beyond our specific model, some of our algorithmic ideas can be applied to other graphical models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.01744v1-abstract-full').style.display = 'none'; document.getElementById('1506.01744v1-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 June, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2015. </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, 3 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/1409.2065">arXiv:1409.2065</a> <span> [<a href="https://arxiv.org/pdf/1409.2065">pdf</a>, <a href="https://arxiv.org/format/1409.2065">other</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 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/10.1371/journal.pcbi.1004228">10.1371/journal.pcbi.1004228 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Convex Clustering: An Attractive Alternative to Hierarchical Clustering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+G+K">Gary K. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Chi%2C+E">Eric Chi</a>, <a href="/search/q-bio?searchtype=author&query=Ranola%2C+J">John Ranola</a>, <a href="/search/q-bio?searchtype=author&query=Lange%2C+K">Kenneth Lange</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="1409.2065v1-abstract-short" style="display: inline;"> The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical cluster… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.2065v1-abstract-full').style.display = 'inline'; document.getElementById('1409.2065v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1409.2065v1-abstract-full" style="display: none;"> The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The current paper exploits the proximal distance principle to construct a novel algorithm for solving the convex clustering problem. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. Our convex clustering software separates parameters, accommodates missing data, and supports prior information on relationships. The software is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.2065v1-abstract-full').style.display = 'none'; document.getElementById('1409.2065v1-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 September, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> PLOS Computational Biology 11(5):e1004228, 2015 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1408.6032">arXiv:1408.6032</a> <span> [<a href="https://arxiv.org/pdf/1408.6032">pdf</a>, <a href="https://arxiv.org/ps/1408.6032">ps</a>, <a href="https://arxiv.org/format/1408.6032">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">stat.ML</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> </div> </div> <p class="title is-5 mathjax"> PMCE: efficient inference of expressive models of cancer evolution with high prognostic power </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Angaroni%2C+F">Fabrizio Angaroni</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kevin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Damiani%2C+C">Chiara Damiani</a>, <a href="/search/q-bio?searchtype=author&query=Caravagna%2C+G">Giulio Caravagna</a>, <a href="/search/q-bio?searchtype=author&query=Graudenzi%2C+A">Alex Graudenzi</a>, <a href="/search/q-bio?searchtype=author&query=Ramazzotti%2C+D">Daniele Ramazzotti</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="1408.6032v3-abstract-short" style="display: inline;"> Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods cannot infer logical formulas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1408.6032v3-abstract-full').style.display = 'inline'; document.getElementById('1408.6032v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1408.6032v3-abstract-full" style="display: none;"> Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods cannot infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution. Results: We introduce PMCE, an expressive framework that leverages mutational profiles from cross-sectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups. Availability: PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript. Contacts: daniele.ramazzotti@unimib.it, alex.graudenzi@ibfm.cnr.it. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1408.6032v3-abstract-full').style.display = 'none'; document.getElementById('1408.6032v3-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 August, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2014. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1306.0505">arXiv:1306.0505</a> <span> [<a href="https://arxiv.org/pdf/1306.0505">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey 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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Diagnosing Heterogeneous Dynamics in Single Molecule/Particle Trajectories with Multiscale Wavelets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kejia Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+B">Bo Wang</a>, <a href="/search/q-bio?searchtype=author&query=Guan%2C+J">Juan Guan</a>, <a href="/search/q-bio?searchtype=author&query=Granick%2C+S">Steve Granick</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="1306.0505v1-abstract-short" style="display: inline;"> We describe a simple automated method to extract and quantify transient heterogeneous dynamical changes from large datasets generated in single molecule/particle tracking experiments. Based on wavelet transform, the method transforms raw data to locally match dynamics of interest. This is accomplished using statistically adaptive universal thresholding, whose advantage is to avoid a single arbitra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1306.0505v1-abstract-full').style.display = 'inline'; document.getElementById('1306.0505v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1306.0505v1-abstract-full" style="display: none;"> We describe a simple automated method to extract and quantify transient heterogeneous dynamical changes from large datasets generated in single molecule/particle tracking experiments. Based on wavelet transform, the method transforms raw data to locally match dynamics of interest. This is accomplished using statistically adaptive universal thresholding, whose advantage is to avoid a single arbitrary threshold that might conceal individual variability across populations. How to implement this multiscale method is described, focusing on local confined diffusion separated by transient transport periods or hopping events, with 3 specific examples: in cell biology, biotechnology, and glassy colloid dynamics. This computationally-efficient method can run routinely on hundreds of millions of data points analyzed within an hour on a desktop personal computer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1306.0505v1-abstract-full').style.display = 'none'; document.getElementById('1306.0505v1-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 June, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2013. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1111.1426">arXiv:1111.1426</a> <span> [<a href="https://arxiv.org/pdf/1111.1426">pdf</a>, <a href="https://arxiv.org/ps/1111.1426">ps</a>, <a href="https://arxiv.org/format/1111.1426">other</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> <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"> SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Roy%2C+R+S">Rajat S. Roy</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K+C">Kevin C. Chen</a>, <a href="/search/q-bio?searchtype=author&query=Sengupta%2C+A+M">Anirvan M. Sengupta</a>, <a href="/search/q-bio?searchtype=author&query=Schliep%2C+A">Alexander Schliep</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="1111.1426v2-abstract-short" style="display: inline;"> Scaffolding is an important subproblem in "de novo" genome assembly in which mate pair data are used to construct a linear sequence of contigs separated by gaps. Here we present SLIQ, a set of simple linear inequalities derived from the geometry of contigs on the line that can be used to predict the relative positions and orientations of contigs from individual mate pair reads and thus produce a c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1111.1426v2-abstract-full').style.display = 'inline'; document.getElementById('1111.1426v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1111.1426v2-abstract-full" style="display: none;"> Scaffolding is an important subproblem in "de novo" genome assembly in which mate pair data are used to construct a linear sequence of contigs separated by gaps. Here we present SLIQ, a set of simple linear inequalities derived from the geometry of contigs on the line that can be used to predict the relative positions and orientations of contigs from individual mate pair reads and thus produce a contig digraph. The SLIQ inequalities can also filter out unreliable mate pairs and can be used as a preprocessing step for any scaffolding algorithm. We tested the SLIQ inequalities on five real data sets ranging in complexity from simple bacterial genomes to complex mammalian genomes and compared the results to the majority voting procedure used by many other scaffolding algorithms. SLIQ predicted the relative positions and orientations of the contigs with high accuracy in all cases and gave more accurate position predictions than majority voting for complex genomes, in particular the human genome. Finally, we present a simple scaffolding algorithm that produces linear scaffolds given a contig digraph. We show that our algorithm is very efficient compared to other scaffolding algorithms while maintaining high accuracy in predicting both contig positions and orientations for real data sets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1111.1426v2-abstract-full').style.display = 'none'; document.getElementById('1111.1426v2-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 November, 2011; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 November, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2011. </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, 6 figures, 7 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/1011.0025">arXiv:1011.0025</a> <span> [<a href="https://arxiv.org/pdf/1011.0025">pdf</a>, <a href="https://arxiv.org/ps/1011.0025">ps</a>, <a href="https://arxiv.org/format/1011.0025">other</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="Analysis of PDEs">math.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> Non-equilibrium allele frequency spectra via spectral methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lukic%2C+S">Sergio Lukic</a>, <a href="/search/q-bio?searchtype=author&query=Hey%2C+J">Jody Hey</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kevin 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="1011.0025v2-abstract-short" style="display: inline;"> A major challenge in the analysis of population genomics data consists of isolating signatures of natural selection from background noise caused by random drift and gene flow. Analyses of massive amounts of data from many related populations require high-performance algorithms to determine the likelihood of different demographic scenarios that could have shaped the observed neutral single nucleoti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1011.0025v2-abstract-full').style.display = 'inline'; document.getElementById('1011.0025v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1011.0025v2-abstract-full" style="display: none;"> A major challenge in the analysis of population genomics data consists of isolating signatures of natural selection from background noise caused by random drift and gene flow. Analyses of massive amounts of data from many related populations require high-performance algorithms to determine the likelihood of different demographic scenarios that could have shaped the observed neutral single nucleotide polymorphism (SNP) allele frequency spectrum. In many areas of applied mathematics, Fourier Transforms and Spectral Methods are firmly established tools to analyze spectra of signals and model their dynamics as solutions of certain Partial Differential Equations (PDEs). When spectral methods are applicable, they have excellent error properties and are the fastest possible in high dimension. In this paper we present an explicit numerical solution, using spectral methods, to the forward Kolmogorov equations for a Wright-Fisher process with migration of K populations, influx of mutations, and multiple population splitting events. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1011.0025v2-abstract-full').style.display = 'none'; document.getElementById('1011.0025v2-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 February, 2011; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 October, 2010; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2010. </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">34 pages, 8 figures, amsart</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0904.2639">arXiv:0904.2639</a> <span> [<a href="https://arxiv.org/pdf/0904.2639">pdf</a>, <a href="https://arxiv.org/ps/0904.2639">ps</a>, <a href="https://arxiv.org/format/0904.2639">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"> FDG-PET Parametric Imaging by Total Variation Minimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Guo%2C+H">Hongbin Guo</a>, <a href="/search/q-bio?searchtype=author&query=Renaut%2C+R">Rosemary Renaut</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kewei Chen</a>, <a href="/search/q-bio?searchtype=author&query=Reiman%2C+E">Eric Reiman</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="0904.2639v1-abstract-short" style="display: inline;"> Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using [18F]-fluorodeoxyglucose positron emission tomography is considered. Traditional imaging is hindered due to low signal to noise ratios at individual voxels. We propose to minimize the total variation of the tracer uptake rates while requiring good fit of traditional Patlak equations. This minimization guarantees spatial… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0904.2639v1-abstract-full').style.display = 'inline'; document.getElementById('0904.2639v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0904.2639v1-abstract-full" style="display: none;"> Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using [18F]-fluorodeoxyglucose positron emission tomography is considered. Traditional imaging is hindered due to low signal to noise ratios at individual voxels. We propose to minimize the total variation of the tracer uptake rates while requiring good fit of traditional Patlak equations. This minimization guarantees spatial homogeneity within brain regions and good distinction between brain regions. Brain phantom simulations demonstrate significant improvement in quality of images by the proposed method as compared to Patlak images with post-filtering using Gaussian or median filters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0904.2639v1-abstract-full').style.display = 'none'; document.getElementById('0904.2639v1-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 April, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2009. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0904.2637">arXiv:0904.2637</a> <span> [<a href="https://arxiv.org/pdf/0904.2637">pdf</a>, <a href="https://arxiv.org/ps/0904.2637">ps</a>, <a href="https://arxiv.org/format/0904.2637">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"> Reducing the noise effects in Logan graphic analysis for PET receptor measurements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Guo%2C+H">Hongbin Guo</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kewei Chen</a>, <a href="/search/q-bio?searchtype=author&query=Renaut%2C+R+A">Rosemary A Renaut</a>, <a href="/search/q-bio?searchtype=author&query=Reiman%2C+E+M">Eric M Reiman</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="0904.2637v1-abstract-short" style="display: inline;"> Logan's graphical analysis (LGA) is a widely-used approach for quantification of biochemical and physiological processes from Positron emission tomography (PET) image data. A well-noted problem associated with the LGA method is the bias in the estimated parameters. We recently systematically evaluated the bias associated with the linear model approximation and developed an alternative to minimiz… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0904.2637v1-abstract-full').style.display = 'inline'; document.getElementById('0904.2637v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0904.2637v1-abstract-full" style="display: none;"> Logan's graphical analysis (LGA) is a widely-used approach for quantification of biochemical and physiological processes from Positron emission tomography (PET) image data. A well-noted problem associated with the LGA method is the bias in the estimated parameters. We recently systematically evaluated the bias associated with the linear model approximation and developed an alternative to minimize the bias due to model error. In this study, we examined the noise structure in the equations defining linear quantification methods, including LGA. The noise structure conflicts with the conditions given by the Gauss-Markov theorem for the least squares (LS) solution to generate the best linear unbiased estimator. By carefully taking care of the data error structure, we propose to use structured total least squares (STLS) to obtain the solution using a one-dimensional optimization problem. Simulations of PET data for [11C] benzothiazole-aniline (Pittsburgh Compound-B [PIB]) show that the proposed method significantly reduces the bias. We conclude that the bias associated with noise is primarily due to the unusual structure of he correlated noise and it can be reduced with the proposed STLS method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0904.2637v1-abstract-full').style.display = 'none'; document.getElementById('0904.2637v1-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 April, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2009. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0904.2634">arXiv:0904.2634</a> <span> [<a href="https://arxiv.org/pdf/0904.2634">pdf</a>, <a href="https://arxiv.org/ps/0904.2634">ps</a>, <a href="https://arxiv.org/format/0904.2634">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"> Model Error Correction for Linear Methods of Reversible Radioligand Binding Measurements in PET Studies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Guo%2C+H">Hongbin Guo</a>, <a href="/search/q-bio?searchtype=author&query=Renaut%2C+R+A">Rosemary A Renaut</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kewei Chen</a>, <a href="/search/q-bio?searchtype=author&query=Reiman%2C+E+M">Eric M Reiman</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="0904.2634v1-abstract-short" style="display: inline;"> Graphical analysis methods are widely used in positron emission tomography quantification because of their simplicity and model independence. But they may, particularly for reversible kinetics, lead to bias in the estimated parameters. The source of the bias is commonly attributed to noise in the data. Assuming a two-tissue compartmental model, we investigate the bias that originates from model… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0904.2634v1-abstract-full').style.display = 'inline'; document.getElementById('0904.2634v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0904.2634v1-abstract-full" style="display: none;"> Graphical analysis methods are widely used in positron emission tomography quantification because of their simplicity and model independence. But they may, particularly for reversible kinetics, lead to bias in the estimated parameters. The source of the bias is commonly attributed to noise in the data. Assuming a two-tissue compartmental model, we investigate the bias that originates from model error. This bias is an intrinsic property of the simplified linear models used for limited scan durations, and it is exaggerated by random noise and numerical quadrature error. Conditions are derived under which Logan's graphical method either over- or under-estimates the distribution volume in the noise-free case. The bias caused by model error is quantified analytically. The presented analysis shows that the bias of graphical methods is inversely proportional to the dissociation rate. Furthermore, visual examination of the linearity of the Logan plot is not sufficient for guaranteeing that equilibrium has been reached. A new model which retains the elegant properties of graphical analysis methods is presented, along with a numerical algorithm for its solution. We perform simulations with the fibrillar amyloid-beta radioligand [11C] benzothiazole-aniline using published data from the University of Pittsburgh and Rotterdam groups. The results show that the proposed method significantly reduces the bias due to model error. Moreover, the results for data acquired over a 70 minutes scan duration are at least as good as those obtained using existing methods for data acquired over a 90 minutes scan duration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0904.2634v1-abstract-full').style.display = 'none'; document.getElementById('0904.2634v1-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 April, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2009. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/physics/0504211">arXiv:physics/0504211</a> <span> [<a href="https://arxiv.org/pdf/physics/0504211">pdf</a>, <a href="https://arxiv.org/ps/physics/0504211">ps</a>, <a href="https://arxiv.org/format/physics/0504211">other</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="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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/10.1103/PhysRevE.72.041909">10.1103/PhysRevE.72.041909 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Solving the Advection-Diffusion Equations in Biological Contexts using the Cellular Potts Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Dan%2C+D">Debasis Dan</a>, <a href="/search/q-bio?searchtype=author&query=Mueller%2C+C">Chris Mueller</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kun Chen</a>, <a href="/search/q-bio?searchtype=author&query=Glazier%2C+J+A">James A. Glazier</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="physics/0504211v1-abstract-short" style="display: inline;"> The Cellular Potts Model (CPM) is a robust, cell-level methodology for simulation of biological tissues and morphogenesis. Both tissue physiology and morphogenesis depend on diffusion of chemical morphogens in the extra-cellular fluid or matrix (ECM). Standard diffusion solvers applied to the cellular potts model use finite difference methods on the underlying CPM lattice. However, these methods… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('physics/0504211v1-abstract-full').style.display = 'inline'; document.getElementById('physics/0504211v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="physics/0504211v1-abstract-full" style="display: none;"> The Cellular Potts Model (CPM) is a robust, cell-level methodology for simulation of biological tissues and morphogenesis. Both tissue physiology and morphogenesis depend on diffusion of chemical morphogens in the extra-cellular fluid or matrix (ECM). Standard diffusion solvers applied to the cellular potts model use finite difference methods on the underlying CPM lattice. However, these methods produce a diffusing field tied to the underlying lattice, which is inaccurate in many biological situations in which cell or ECM movement causes advection rapid compared to diffusion. Finite difference schemes suffer numerical instabilities solving the resulting advection-diffusion equations. To circumvent these problems we simulate advection-diffusion within the framework of the CPM using off-lattice finite-difference methods. We define a set of generalized fluid particles which detach advection and diffusion from the lattice. Diffusion occurs between neighboring fluid particles by local averaging rules which approximate the Laplacian. Directed spin flips in the CPM handle the advective movement of the fluid particles. A constraint on relative velocities in the fluid explicitly accounts for fluid viscosity. We use the CPM to solve various diffusion examples including multiple instantaneous sources, continuous sources, moving sources and different boundary geometries and conditions to validate our approximation against analytical and established numerical solutions. We also verify the CPM results for Poiseuille flow and Taylor-Aris dispersion. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('physics/0504211v1-abstract-full').style.display = 'none'; document.getElementById('physics/0504211v1-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> 28 April, 2005; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2005. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. E 72, 041909 (2005) (10 pages) </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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