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arXiv 2021年

作者： Dey, Vishal Machiraju, Raghu Ning, Xia Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Biomedical Informatics The Ohio State University ColumbusOH United States Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Recent advances in molecular machine learning, especially deep neural networks such as Graph Neural Networks (GNNs) for predicting structure activity relationships (SAR) have shown tremendous potential in computer-aided drug discovery. However, the applicability of such deep neural networks are limited by the requirement of large amounts of training data. In order to cope with limited training data for a target task, transfer learning for SAR modeling has been recently adopted to leverage information from data of related tasks. In this work, in contrast to the popular parameter-based transfer learning such as pretraining, we develop novel deep transfer learning methods TAc and TAc-fc to leverage source domain data and transfer useful information to the target domain. TAc learns to generate effective molecular features that can generalize well from one domain to another, and increase the classification performance in the target domain. Additionally, TAc-fc extends TAc by incorporating novel components to selectively learn feature-wise and compound-wise transferability. We used the bioassay screening data from PubChem, and identified 120 pairs of bioassays such that the active compounds in each pair are more similar to each other compared to its inactive compounds. Overall, TAc achieves the best performance with average ROC-AUC of 0.801;it significantly improves ROC-AUC of 83% target tasks with average task-wise performance improvement of 7.102%, compared to the best baseline FCN-dmpna (DT). Our experiments clearly demonstrate that TAc achieves significant improvement over all baselines across a large number of target tasks. Furthermore, although TAc-fc achieves slightly worse ROC-AUC on average compared to TAc (0.798 vs 0.801), TAc-fc still achieves the best performance on more tasks in terms of PR-AUC and F1 compared to other methods. In summary, TAc-fc is also found to be a strong model with competitive or even better performance than TAc on a notable number of tar

关键词： Deep neural networks

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Psychological and behavioural considerations for integrating polygenic risk scores for disease into clinical practice

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Nature human behaviour 2025年 2025 May 12页

作者： Saskia C Sanderson Michael Inouye Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London UK. saskia.sanderson@kcl.ac.uk. Department of Behavioural Science and Health University College London London UK. saskia.sanderson@kcl.ac.uk. Mental Health Mission National Institute for Health and Care Research (NIHR) Mental Health Translational Research Collaboration London UK. saskia.sanderson@kcl.ac.uk. Public Health Genomics Foundation Cambridge UK. saskia.sanderson@kcl.ac.uk. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Hinxton UK. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia.

A polygenic risk score (PRS) summarizes in one number an individual's estimated genetic association with a specific trait or disease based on the common DNA variants included in the score. Disease PRSs have the potential to positively affect population health by improving disease risk prediction, thereby also potentially improving disease prevention, early intervention and treatment. However, given the potential psychological, behavioural and other harms, there are also concerns about integrating PRSs into clinical tools and healthcare systems. Here we assess five arguments against implementing PRSs for physical disease in clinical practice that revolve around psychological and behavioural considerations. For each argument, we consider a counterargument, the evidence and underlying theory, any gaps in the evidence base and possible future directions and research priorities. We conclude that, although there may be other barriers to implementation, there is currently little evidence of psychological or behavioural harms from integrating PRSs into practice.

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Modeling Path Importance for Effective Alzheimer’s Disease Drug Repurposing

arXiv

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arXiv 2023年

作者： Xiang, Shunian Lawrence, Patrick J. Peng, Bo Chiang, ChienWei Kim, Dokyoon Shen, Li Ning, Xia Biomedical Informatics Department The Ohio State University ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University ColumbusOH43210 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

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. © 2023, CC BY.

关键词： Embeddings

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SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search

arXiv

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arXiv 2024年

作者： Du, Hanwen Peng, Bo Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Division of Medicinal Chemistry and Pharmacognosy College of Pharmacy The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods train Reinforcement Learning (RL)-based agent with greedy action selection or sampling strategy, and may suffer from suboptimal conversational planning. To address this, we present a novel Monte Carlo Tree Search (MCTS)-based CRS framework SAPIENT. SAPIENT consists of a conversational agent (S-agent) and a conversational planner (S-planner). S-planner builds a conversational search tree with MCTS based on the initial actions proposed by S-agent to find conversation plans. The best conversation plans from S-planner are used to guide the training of S-agent, creating a self-training loop where S-agent can iteratively improve its capability for conversational planning. Furthermore, we propose an efficient variant SAPIENT-e for trade-off between training efficiency and performance. Extensive experiments on four benchmark datasets validate the effectiveness of our approach, showing that SAPIENT outperforms the state-of-the-art baselines. © 2024, CC BY.

关键词： Chatbots

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Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele

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Nature communications 2025年第1期16卷 3470-3470页

作者： Zhang, Xinruo Brody, Jennifer A. Graff, Mariaelisa Highland, Heather M. Chami, Nathalie Xu, Hanfei Wang, Zhe Ferrier, Kendra R. Chittoor, Geetha Josyula, Navya Shilpa Meyer, Mariah Gupta, Shreyash Li, Xihao Li, Zilin Allison, Matthew A. Becker, Diane M. Bielak, Lawrence F. Bis, Joshua C. Boorgula, Meher Preethi Bowden, Donald W. Broome, Jai G. Buth, Erin J. Carlson, Christopher S. Chang, Kyong-Mi Chavan, Sameer Chiu, Yen-Feng Chuang, Lee-Ming Conomos, Matthew P. DeMeo, Dawn L. Du, Mengmeng Duggirala, Ravindranath Eng, Celeste Fohner, Alison E. Freedman, Barry I. Garrett, Melanie E. Guo, Xiuqing Haiman, Chris Heavner, Benjamin D. Hidalgo, Bertha Hixson, James E. Ho, Yuk-Lam Hobbs, Brian D. Hu, Donglei Hui, Qin Hwu, Chii-Min Jackson, Rebecca D. Jain, Deepti Kalyani, Rita R. Kardia, Sharon L R Kelly, Tanika N. Lange, Ethan M. LeNoir, Michael Li, Changwei Le Marchand, Loic McDonald, Merry-Lynn N McHugh, Caitlin P. Morrison, Alanna C. Naseri, Take Palmer, Nicholette D. Pankow, James S. Perry, James A. Peters, Ulrike Preuss, Michael H. Rao, D.C. Regan, Elizabeth A. Reupena, Sefuiva M. Roden, Dan M. Rodriguez-Santana, Jose Sitlani, Colleen M. Smith, Jennifer A. Tiwari, Hemant K. Vasan, Ramachandran S. Wang, Zeyuan Weeks, Daniel E. Wessel, Jennifer Wiggins, Kerri L. Wilkens, Lynne R. Wilson, Peter W F Yanek, Lisa R. Yoneda, Zachary T. Zhao, Wei Zöllner, Sebastian Arnett, Donna K. Ashley-Koch, Allison E. Barnes, Kathleen C. Blangero, John Boerwinkle, Eric Burchard, Esteban G. Carson, April P. Chasman, Daniel I. Ida Chen, Yii-Der Curran, Joanne E. Fornage, Myriam Gordeuk, Victor R. He, Jiang Heckbert, Susan R. Hou, Lifang Irvin, Marguerite R. Kooperberg, Charles Minster, Ryan L. Mitchell, Braxton D. Nouraie, Mehdi Psaty, Bruce M. Raffield, Laura M. Reiner, Alexander P. Rich, Stephen S. Rotter, Jerome I. Benjamin Shoemaker, M. Smith, Nicholas L. Taylor, Kent D. Telen, Marilyn J. Weiss, Scott T. Zhang, Yingze Heard-Costa, Nancy Sun, Yan V. Lin, Xihong Cupples, L Adrienne Lange, Leslie A. Liu, Ching-Ti Loos, Ruth J F North, K Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA The Mindich Child Health and Development Institute Icahn School of Medicine at Mount Sinai New York NY USA Department of Biostatistics School of Public Health Boston University Boston MA United States Division of Biomedical Informatics and Personalized Medicine University of Colorado School of Medicine Anschutz Medical Campus Aurora CO United States Population Health Sciences Geisinger Danville PA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Department of Biostatistics Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Genetics School of Medicine University of North Carolina at Chapel Hill Chapel Hill NC United States Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis IN United States School of Mathematics and Statistics and KLAS Northeast Normal University Changchun Jilin China Department of Family Medicine Division of Preventive Medicine University of California San Diego La Jolla CA United States Department of Medicine General Internal Medicine Johns Hopkins University School of Medicine Baltimore MD United States Department of Epidemiology School of Public Health University of Michigan Ann Arbor MI United States Department of Medicine University of Colorado School of Medicine Aurora CO United States Department of Biochemistry Wake Forest School of Medicine Winston-Salem NC United States Department of Biostatistics School of Public Health University of Washington Seattle WA United States Department of

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine. © 2025. The Author(s).

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Can Researchers Assess the Suitability of datasets to Answer Their Research Questions, with Access to Metadata Only? 18

Can Researchers Assess the Suitability of Datasets to Answer...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Tilston, George Williams, Richard Griffiths, Emily Al-Adely, Sarah Lawson-Tovey, Saskia Hulme, Will Short, Andrea Davies, Jim Welch, James Peek, Niels Centre for Health Informatics Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester ManchesterM13 9GB United Kingdom Nihr Manchester Biomedical Research Centre The University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Nihr Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Centre for Epidemiology Versus Arthritis The University of Manchester Manchester United Kingdom Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research The University of Manchester Manchester United Kingdom Division of Human Communication and Hearing School of Health Sciences The University of Manchester Manchester United Kingdom Nihr Oxford Biomedical Research Centre Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Old Road Campus Oxford United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9781643682648

Health research increasingly requires effective ways to identify existing datasets and assess their suitability for research. We sought to test whether researchers could use an existing metadata catalogue to assess the suitability of datasets for addressing specified research questions. Five datasets were described in the National Institute for Health Research Health informatics Collaborative metadata catalogue, and for each dataset five associated research questions were formulated, some of which were answerable with the dataset while others were not. Thirteen researchers each assessed whether the ten questions associated with two randomly selected datasets were answerable with the described datasets. After removing instances where participants misunderstood the question or lacked subject matter knowledge to make the assessment, we found that 87 out of 109 assessments (80%) were correct. Participants particularly struggled with one dataset which consisted of EHR data. The most common reason for incorrect assessments was the inability to find the relevant information in the metadata catalogue. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Metadata

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Ranking-based convolutional neural network models for peptide-MHC binding prediction

arXiv

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arXiv 2020年

作者： Chen, Ziqi Min, Martin Renqiang Ning, Xia Computer Science and Engineering Department Ohio State University ColumbusOH United States Machine Learning Department NEC Labs America PrincetonNJ United States Biomedical Informatics Department Ohio State University ColumbusOH United States Translational Data Analytics Institute Ohio State University ColumbusOH United States

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network (CNN)based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles. © 2020, CC BY.

关键词： Peptides

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Effectiveness and cost of recruiting healthy volunteers for clinical research studies using an electronic patient portal: A randomized study

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Journal of clinical and translational science 2017年第6期1卷 366-372页

作者： Mary H Samuels Robert Schuff Peter Beninato Adriel Gorsuch James Dursch Sarah Egan Bridget Adams Kate F Hollis Rachel Navarro Timothy E Burdick Oregon Clinical and Translational Research Institute Oregon Health & Science University Portland OR USA. Information Technology Group Oregon Health & Science University Portland OR USA. Office of the Chief Privacy Officer Oregon Health & Science University Portland OR USA. University Libraries University of Denver Denver CO USA. Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University Portland OR USA. Departments of Community and Family Medicine and Biomedical Data Science Geisel School of Medicine at Dartmouth Portland OR USA.

INTRODUCTION:It is not clear how to effectively recruit healthy research volunteers. METHODS:We developed an electronic health record (EHR)-based algorithm to identify healthy subjects, who were randomly assigned to receive an invitation to join a research registry via the EHR's patient portal, letters, or phone calls. A follow-up survey assessed contact preferences. RESULTS:The EHR algorithm accurately identified 858 healthy subjects. Recruitment rates were low, but occurred more quickly via the EHR patient portal than letters or phone calls (2.7 vs. 19.3 or 10.4 d). Effort and costs per enrolled subject were lower for the EHR patient portal (3.0 vs. 17.3 or 13.6 h, $113 vs. $559 or $435). Most healthy subjects indicated a preference for contact via electronic methods. CONCLUSIONS:Healthy subjects can be accurately identified from EHR data, and it is faster and more cost-effective to recruit healthy research volunteers using an EHR patient portal.

关键词： Patient selection clinical research electronic health records healthy volunteers recruitment

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An LLM-Driven Multi-Agent Debate System for Mendelian Diseases

arXiv

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arXiv 2025年

作者： Zhou, Xinyang Ren, Yongyong Zhao, Qianqian Huang, Daoyi Wang, Xinbo Zhao, Tingting Zhu, Zhixing He, Wenyuan Li, Shuyuan Xu, Yan Sun, Yu Yu, Yongguo Wu, Shengnan Wang, Jian Yu, Guangjun He, Dake Ban, Bo Lu, Hui Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200240 China SJTU-Yale Joint Center for Biostatistics and Data Science Technical Center for Digital Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai200240 China Department of Endocrinology Affiliated Hospital of Jining Medical University Jining Medical University Shandong Jining272029 China Shanghai Children’s Hospital School of Medicine Shanghai Jiao Tong University Shanghai200062 China The Second Affiliated Hospital School of Medicine The Chinese University of Hong Kong Shenzhen & Longgang District People’s Hospital of Shenzhen Shenzhen518172 China Center for Biomedical Informatics Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai Children's Hospital Shanghai200062 China International Peace Maternity and Child Health Hospital School of Medicine Shanghai Jiao Tong University Shanghai200003 China Department of Medical Genetics and Molecular Diagnostic Laboratory Shanghai Children's Medical Center Shanghai Jiao Tong University School of Medicine Shanghai200127 China Clinical Genetics Center Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai200092 China Shanghai Institute for Pediatric Research Shanghai200092 China Department of Children Neurology Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai200092 China

Accurate diagnosis of Mendelian diseases is crucial for precision therapy and assistance in preimplantation genetic diagnosis. However, existing methods often fall short of clinical standards or depend on extensive datasets to build pretrained machine learning models. To address this, we introduce an innovative LLM-Driven multi-agent debate system (MD2GPS) with natural language explanations of the diagnostic results. It utilizes a language model to transform results from data-driven and knowledge-driven agents into natural language, then fostering a debate between these two specialized agents. This system has been tested on 1,185 samples across four independent datasets, enhancing the TOP1 accuracy from 42.9% to 66% on average. Additionally, in a challenging cohort of 72 cases, MD2GPS identified potential pathogenic genes in 12 patients, reducing the diagnostic time by 90%. The methods within each module of this multi-agent debate system are also replaceable, facilitating its adaptation for diagnosing and researching other complex diseases. © 2025, CC BY.

关键词： Disease control

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T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy 27th

T-Cell Receptor Optimization with Reinforcement Learning an...

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27th International Conference on Research in Computational Molecular Biology, RECOMB 2023

作者： Chen, Ziqi Min, Martin Renqiang Guo, Hongyu Cheng, Chao Clancy, Trevor Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs PrincetonNJ08540 United States Digital Technologies Research Centre National Research Council Canada Ontario Canada Department of Medicine Baylor College of Medicine HoustonTX77030 United States NEC Oncolmmunity AS Oslo Cancer Cluster Innovation Park Ullernchausséen 64 Oslo0379 Norway Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9783031291180

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (Formula Presented) problem, and presented a framework (Formula Presented) with a mutation policy using proximal policy optimization. (Formula Presented) mutates TCRs into effective ones that can recognize given peptides. (Formula Presented) leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared (Formula Presented) with multiple baseline methods and demonstrated that (Formula Presented) significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of(Formula Presented) for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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