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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="Peng, B"> <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" 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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.13522">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/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/2411.03320">arXiv:2411.03320</a> <span> [<a href="https://arxiv.org/pdf/2411.03320">pdf</a>, <a href="https://arxiv.org/format/2411.03320">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"> log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Hu%2C+X">Xiao Hu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Z">Ziqi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+B">Bo Peng</a>, <a href="/search/q-bio?searchtype=author&query=Adu-Ampratwum%2C+D">Daniel Adu-Ampratwum</a>, <a href="/search/q-bio?searchtype=author&query=Ning%2C+X">Xia Ning</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.03320v4-abstract-short" style="display: inline;"> Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based frame… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03320v4-abstract-full').style.display = 'inline'; document.getElementById('2411.03320v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03320v4-abstract-full" style="display: none;"> Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based framework designed for predicting chemical reaction yields. A key feature of log-RRIM is its integration of a cross-attention mechanism that focuses on the interplay between reagents and reaction centers. This design reflects a fundamental principle in chemical reactions: the crucial role of reagents in influencing bond-breaking and formation processes, which ultimately affect reaction yields. log-RRIM also implements a local-to-global reaction representation learning strategy. This approach initially captures detailed molecule-level information and then models and aggregates intermolecular interactions. Through this hierarchical process, log-RRIM effectively captures how different molecular fragments contribute to and influence the overall reaction yield, regardless of their size variations. log-RRIM shows superior performance in our experiments, especially for medium to high-yielding reactions, proving its reliability as a predictor. The framework's sophisticated modeling of reactant-reagent interactions and precise capture of molecular fragment contributions make it a valuable tool for reaction planning and optimization in chemical synthesis. The data and codes of log-RRIM are accessible through https://github.com/ninglab/Yield_log_RRIM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03320v4-abstract-full').style.display = 'none'; document.getElementById('2411.03320v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">45 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.15211">arXiv:2310.15211</a> <span> [<a href="https://arxiv.org/pdf/2310.15211">pdf</a>, <a href="https://arxiv.org/format/2310.15211">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> </div> <p class="title is-5 mathjax"> Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xiang%2C+S">Shunian Xiang</a>, <a href="/search/q-bio?searchtype=author&query=Lawrence%2C+P+J">Patrick J. Lawrence</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+B">Bo Peng</a>, <a href="/search/q-bio?searchtype=author&query=Chiang%2C+C">ChienWei Chiang</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+D">Dokyoon Kim</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+L">Li Shen</a>, <a href="/search/q-bio?searchtype=author&query=Ning%2C+X">Xia Ning</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.15211v2-abstract-short" style="display: inline;"> Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15211v2-abstract-full').style.display = 'inline'; document.getElementById('2310.15211v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.15211v2-abstract-full" style="display: none;"> Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15211v2-abstract-full').style.display = 'none'; document.getElementById('2310.15211v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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, 3 figures, 2 tables, 1 supplementary figure, 5 supplementary tables, Preprint of an article accepted for publication in Pacific Symposium on Biocomputing 漏2023 World Scientific Publishing Co., Singapore, http://psb.stanford.edu/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.11890">arXiv:2308.11890</a> <span> [<a href="https://arxiv.org/pdf/2308.11890">pdf</a>, <a href="https://arxiv.org/format/2308.11890">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Z">Ziqi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+B">Bo Peng</a>, <a href="/search/q-bio?searchtype=author&query=Parthasarathy%2C+S">Srinivasan Parthasarathy</a>, <a href="/search/q-bio?searchtype=author&query=Ning%2C+X">Xia Ning</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="2308.11890v3-abstract-short" style="display: inline;"> Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules. In this paper, we formulated an in silico shape-conditioned molecule generation problem to generate 3D molecule structures conditioned on the shape of a given molecule. To address this problem, we developed a translation- and rotation-equivariant shape-guided generative model ShapeMol. Sh… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.11890v3-abstract-full').style.display = 'inline'; document.getElementById('2308.11890v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.11890v3-abstract-full" style="display: none;"> Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules. In this paper, we formulated an in silico shape-conditioned molecule generation problem to generate 3D molecule structures conditioned on the shape of a given molecule. To address this problem, we developed a translation- and rotation-equivariant shape-guided generative model ShapeMol. ShapeMol consists of an equivariant shape encoder that maps molecular surface shapes into latent embeddings, and an equivariant diffusion model that generates 3D molecules based on these embeddings. Experimental results show that ShapeMol can generate novel, diverse, drug-like molecules that retain 3D molecular shapes similar to the given shape condition. These results demonstrate the potential of ShapeMol in designing drug candidates of desired 3D shapes binding to protein target pockets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.11890v3-abstract-full').style.display = 'none'; document.getElementById('2308.11890v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 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/2002.07699">arXiv:2002.07699</a> <span> [<a href="https://arxiv.org/pdf/2002.07699">pdf</a>, <a href="https://arxiv.org/format/2002.07699">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey 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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Peng%2C+B">Bo Peng</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+X">Xiaohui Yao</a>, <a href="/search/q-bio?searchtype=author&query=Risacher%2C+S+L">Shannon L. Risacher</a>, <a href="/search/q-bio?searchtype=author&query=Saykin%2C+A+J">Andrew J. Saykin</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+L">Li Shen</a>, <a href="/search/q-bio?searchtype=author&query=Ning%2C+X">Xia Ning</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="2002.07699v5-abstract-short" style="display: inline;"> Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are no… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07699v5-abstract-full').style.display = 'inline'; document.getElementById('2002.07699v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.07699v5-abstract-full" style="display: none;"> Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed learning-to-rank approach PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend PLTR to better separate the most effective cognitive assessments and the less effective ones. Results: Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions: The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07699v5-abstract-full').style.display = 'none'; document.getElementById('2002.07699v5-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted by BMC MIDM</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" 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