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arXiv 2024年

作者： Sankar, Baradwaj Simha Gilliland, Destiny Rincon, Jack Hermjakob, Henning Yan, Yu Adam, Irsyad Lemaster, Gwyneth Wang, Dean Watson, Karol Bui, Alex Wang, Wei Ping, Peipei Department of Physiology University of California Los AngelesCA United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Los AngelesCA United States Cambridge United Kingdom Bioinformatics IDP University of California Los AngelesCA United States University of California Los AngelesCA United States Medical Informatics Home Area University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States

Foundational Models (FMs) are gaining increasing attention in the biomedical AI ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. © 2024, CC BY.

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Machine learning for early prediction of circulatory failure in the intensive care unit

arXiv

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arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Validation Requirements for AI-based Intervention-Evaluation in Aging and Longevity Research and Practice

arXiv

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arXiv 2024年

作者： Fuellen, Georg Kulaga, Anton Lobentanzer, Sebastian Unfried, Maximilian Avelar, Roberto A. Palmer, Daniel Kennedy, Brian K. Institute for Biostatistics and Informatics in Medicine and Ageing Research Rostock University Medical Center Rostock Germany UCD Conway Institute of Biomolecular and Biomedical Research School of Medicine University College Dublin Dublin Ireland Institute for Computational Biomedicine Heidelberg University Faculty of Medicine Heidelberg University Hospital Heidelberg Germany European Bioinformatics Institute Hinxton Cambridgeshire United Kingdom Healthy Longevity Translational Research Program Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Physiology Yong Loo Lin School of Medicine National University of Singapore Singapore

The field of aging and longevity research is overwhelmed by vast amounts of data, calling for the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the evaluation of geroprotective interventions. Such evaluations should be correct, useful, comprehensive, explainable, and they should consider causality, interdisciplinarity, adherence to standards, longitudinal data and known aging biology. In particular, comprehensive analyses should go beyond comparing data based on canonical biomedical databases, suggesting the use of AI to interpret changes in biomarkers and outcomes. Our requirements motivate the use of LLMs with Knowledge Graphs and dedicated workflows employing, e.g., Retrieval-Augmented Generation. While naive trust in the responses of AI tools can cause harm, adding our requirements to LLM queries can improve response quality, calling for benchmarking efforts and justifying the informed use of LLMs for advice on longevity *** Codes 68T01 General topics in artificial intelligence © 2024, CC BY.

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Differential regulation of JAK1 expression by ETS1 associated with predisposition to primary biliary cholangitis

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Journal of Genetics and Genomics 2023年第10期50卷 807-812页

作者： Peng Jiang Chan Wang Mingming Zhang Ye Tian Weifeng Zhao Junyi Xin Yexi Huang Zhibin Zhao Wenjuan Sun Jie Long Ruqi Tang Fang Qiu Xingjuan Shi Yi Zhao Li Zhu Na Dai Lei Liu Xudong Wu Jinshan Nie Bo Jiang Youlin Shao Yueqiu Gao Jianjiang Yu Zhigang Hu Zhidong Zang Yuhua Gong Yaping Dai Lan Wang Ningling Ding Ping Xu Sufang Chen Lu Wang Jing Xu Luyao Zhang Junyan Hong Ruonan Qian Hu Li Xuan Jiang Congwei Chen Wenyan Tian Jian Wu Yuzhang Jiang Chongxu Han Kui Zhang Hong Qiu Li Li Hong Fan Liming Chen Jianqiong Zhang Zhongsheng Sun Xiao Han Zhenhua Dai Erguang Li M.Eric Gershwin Zhexiong Lian Xiong Ma Michael F.Seldin Weichang Chen Meilin Wang Xiangdong Liu Key Laboratory of Developmental Genes and Human Diseases School of Life Science and TechnologySoutheast UniversityNanjingJiangsu 210096China Institute of Translational Medicine Yangzhou University Medical CollegeYangzhouJiangsu 225009China Department of Radiology&Oncology The Second Affiliated Hospital of Soochow UniversitySuzhouJiangsu 215004China The First Affiliated Hospital of Soochow University SuzhouJiangsu 215006China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjingJiangsu 210029China Author links open overlay panelYexi Huang Guangdong Provincial People's Hospital Guangdong Academy of Medical SciencesGuangzhouGuangdong 510080China Department of Gastroenterology and Hepatology Shanghai Institute of Digestive DiseasesShanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShanghai 200001China Department of Laboratory Medicine The Fourth Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210031China Department of Gastrointestinal Endoscopy Eastern Hepatobiliary Surgery HospitalShanghai 201800China The Fifth People's Hospital Soochow UniversitySuzhouJiangsu 215007China Department of Gastroenterology Jiangsu University Affiliated Kunshan HospitalKunshanJiangsu 215300China Department of Gastroenterology Yixing People's HospitalYixinJiangsu 214200China Department of Gastroenterology Yancheng First People's HospitalYanchengJiangsu 224005China Department of Gastroenterology Taicang First People's HospitalSoochow UniversityTaicangJiangsu 215400China Department of Hepatology Jingjiang Second People's HospitalJingjiangJiangsu 214500China Department of Hepatology The Third People's Hospital of ChangzhouChangzhouJiangsu 213001China Department of Liver Diseases Shuguang HospitalShanghai University of Traditional Chinese MedicineShanghai 201203China Department of Laboratory Medicine Jiangyin People's HospitalSoutheast UniversityJiangyinJiangsu 214400China Department of Labora

Primary biliary cholangitis(PBC)is an autoimmune liver disease characterized by the destruction of intrahepatic small bile ducts and progressive cholestasis,eventually leading to liver cirrhosis and hepatic failure wi... 详细信息

关键词： cholangitis biliary

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Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder

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Nature Genetics 2025年 1-13页

作者： Strom, Nora I. Gerring, Zachary F. Galimberti, Marco Yu, Dongmei Halvorsen, Matthew W. Abdellaoui, Abdel Rodriguez-Fontenla, Cristina Sealock, Julia M. Bigdeli, Tim Coleman, Jonathan R. Mahjani, Behrang Thorp, Jackson G. Bey, Katharina Burton, Christie L. Luykx, Jurjen J. Zai, Gwyneth Alemany, Silvia Andre, Christine Askland, Kathleen D. Bäckman, Julia Banaj, Nerisa Barlassina, Cristina Nissen, Judith Becker Bienvenu, O. Joseph Black, Donald Bloch, Michael H. Børte, Sigrid Bosch, Rosa Breen, Michael Brennan, Brian P. Brentani, Helena Buxbaum, Joseph D. Bybjerg-Grauholm, Jonas Byrne, Enda M. Cabana-Dominguez, Judit Camarena, Beatriz Camarena, Adrian Cappi, Carolina Carracedo, Angel Casas, Miguel Cavallini, Maria Cristina Ciullo, Valentina Cook, Edwin H. Crosby, Jesse Cullen, Bernadette A. De Schipper, Elles J. Delorme, Richard Djurovic, Srdjan Elias, Jason A. Estivill, Xavier Falkenstein, Martha J. Fundin, Bengt T. Garner, Lauryn Gironda, Christina Goes, Fernando S. Grados, Marco A. Grove, Jakob Guo, Wei Haavik, Jan Hagen, Kristen Harrington, Kelly Havdahl, Alexandra Höffler, Kira D. Hounie, Ana G. Hucks, Donald Hultman, Christina Janecka, Magdalena Jenike, Eric Karlsson, Elinor K. Kelley, Kara Klawohn, Julia Krasnow, Janice E. Krebs, Kristi Lange, Christoph Lanzagorta, Nuria Levey, Daniel Lindblad-Toh, Kerstin Macciardi, Fabio Maher, Brion Mathes, Brittany McArthur, Evonne McGregor, Nathaniel McLaughlin, Nicole C. Meier, Sandra Miguel, Euripedes C. Mulhern, Maureen Nestadt, Paul S. Nurmi, Erika L. O’Connell, Kevin S. Osiecki, Lisa Ousdal, Olga Therese Palviainen, Teemu Pedersen, Nancy L. Piras, Federica Potluri, Sriramya Rabionet, Raquel Ramirez, Alfredo Rauch, Scott Reichenberg, Abraham Riddle, Mark A. Ripke, Stephan Rosário, Maria C. Sampaio, Aline S. Schiele, Miriam A. Skogholt, Anne Heidi Sloofman, Laura G. Smit, Jan Artigas, María Soler Thomas, Laurent F. Tifft, Eric Vallada, Homero van Kirk, Nathanial Veenstra-VanderWeele, Jeremy Vulink, Nienke N. Walker, Christopher P. Wang, Ying Wendland, Jens R. Winsvold Department of Psychology Humboldt-Universität zu Berlin Berlin Germany Department of Psychiatric Phenomics and Genomics (IPPG) Ludwig-Maximilians University Munich Munich Germany Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet and Stockholm Health Services Region Stockholm Stockholm Sweden Department of Biomedicine Aarhus University Aarhus Denmark University Hospital of Psychiatry and Psychotherapy University of Bern Bern Switzerland Department of Mental Health and Neuroscience Translational Neurogenomics QIMR Berghofer Medical Research Institute Brisbane QLD Australia Department of Population Health and Immunity Healthy Development and Ageing Walter and Eliza Hall Institute of Medical Research Melbourne VIC Australia Department of Psychiatry Human Genetics Yale University New Haven CT United States VA Connecticut Healthcare System West Haven CT United States Department of Center for Genomic Medicine Psychiatric and Neurodevelopmental Genetics Unit Massachusetts General Hospital Boston MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA United States Department of Genetics University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Psychiatry Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands Center for Research in Molecular Medicine and Chronic Diseases (CiMUS) Genomics and Bioinformatics University of Santiago de Compostela Santiago de Compostela Spain Grupo de Medicina Xenómica Genetics Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS) Santiago de Compostela Spain Vanderbilt Genetics Institute Vanderbilt University Nashville TN United States Department of Psychiatry and Behavioral Sciences SUNY Downstate Health Sciences University Brooklyn NY United States VA NY Harbor Healthcare System Brooklyn NY United States Social Genetic and Developmental Psychiatry Centre Insti

Obsessive–compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D₁ and D₂ type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index. © The Author(s) 2025.

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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A pilot study for genome wide association study (GWAS) in patients with juvenile idiopathic arthritis (JIA) and their parents

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Pediatric Rheumatology 2011年第1期9卷 1-1页

作者： Seza Ozen Yelda Bilginer Nesrin Besbas Yesim Aydin Son Yener Tuncel Erkan Demirkaya Cengizhan Acikel Bilge Volkan Salancı Mehmet Alikasifoglu Ozgur Kasapcopur Erbil Unsal Hacettepe University Faculty of Medicine Department of Pediatrics Pediatric Nephrology and Rheumatology Unit Ankara Turkey Department of Health Informatics METU Informatics Institute Ankara Turkey METU Informatics Institute Bioinformatics Graduate Program Ankara Turkey Gulhane Military Medical Academy Pediatric Nephrology & Rheumatology Unit Ankara Turley Department of Public Health Division of Epidemiology Gulhane Military Medical Faculty Ankara Turkey Department of Pediatrics Clinical Genetics Unit Hacettepe University Faculty of Medicine Ankara Turkey Istanbul University Cerrahpasa Medical School Department of Pediatric Rheumatology Istanbul Turley Department of Pediatrics Division of Immunology- Rheumatology Dokuz Eylul University Faculty of Medicine Izmir Turkey

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

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arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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Correction to 'TMSNP: a web server to predict pathogenesis of missense mutations in the transmembrane region of membrane proteins'

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NAR genomics and bioinformatics 2021年第3期3卷 lqab076页

作者： Adrián Garcia-Recio José Carlos Gómez-Tamayo Iker Reina Mercedes Campillo Arnau Cordomí Mireia Olivella Laboratori de Medicina Computacional Facultat de Medicina Universitat Autònoma de Barcelona 08193 Bellaterra Spain. Pharmacoinformatics Group Research Program on Biomedical Informatics (IMIM/UPF) 08003 Barcelona Spain. Bioinformatics and Medical Statistics Group Facultat de Ciències i Tecnologia UVIC-UCC 08500 Vic Barcelona Spain.

[This corrects the article DOI: 10.1093/nar/lqab008.].

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