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arXiv 2023年

作者： Wu, Da Yang, Jingye Liu, Cong Hsieh, Tzung-Chien Marchi, Elaine Blair, Justin Krawitz, Peter Weng, Chunhua Chung, Wendy Lyon, Gholson J. Krantz, Ian D. Kalish, Jennifer M. Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Institute for Genomic Statistics and Bioinformatics University Hospital Bonn Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Human Genetics New York State Institute for Basic Research in Developmental Disabilities Staten IslandNY United States Division of Human Genetics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Pediatrics Boston Children’s Hospital Harvard Medical School BostonMA United States Biology PhD Program The Graduate Center The City University of New York New York United States Department of Genetics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pediatrics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data. © 2023, CC BY.

关键词： Diseases

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Genome-wide association study of long COVID

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Nature Genetics 2025年 1-16页

作者： Vilma Lammi Tomoko Nakanishi Samuel E. Jones Juha Karjalainen Martin Broberg Hele H. Haapaniemi Matti Pirinen Mari E. K. Niemi Mattia Cordioli Mark J. Daly Andrea Ganna Hanna M. Ollila J. Brent Richards Yukinori Okada Shea J. Andrews Beatriz Cortés Rafael de Cid Susana Iraola-Guzmán Natalia Blay Xavier Farré Heath E. O’Brien Thompson Hannah Patrick J. Short Ana Ochoa-Guzman Michelle Duran-Gomez Brian E. Fulton-Howard Masahiro Kanai Axel Schmidt Daniella Balla Julia Heggemann Sonja Schultz Pari Behzad Markus M. Nöthen Abigail Miller Kerstin U. Ludwig Ruth E. Mitchell George Davey Smith Nicholas J. Timpson Abdou Mousas Evangelos Evangelou Evangelia Ntzani Konstantinos K. Tsilidis Massimo Mangino Alicia Huerta-Chagoya Nasa Sinnott-Armstrong Elizabeth T. Cirulli Francisco Tanudjaja Efren Sandoval Nicole L. Washington Simon White Alexandre Bolze Kelly M. Schiabor Barrett Marc Vaudel Alex S. F. Kwong Amit K. Maiti Minttu M. Marttila Daniel C. Posner Alexis A. Rodriguez Chiara Batini Beatriz Guillen-Guio Olivia C. Leavy Anna L. Guyatt Catherine John Louise V. Wain Francesca Minnai Anna R. Dearman Benedict Hignell Meena Kumari C. A. Robert Warmerdam Lude H. Franke Celia B. Sequeros Søren Brunak David Westergaard Karina Banasik Thomas W. Winkler Iris M. Heid Daniel M. Jordan Ryan C. Thompson Alexander W. Charney Laura G. Sloofman Nicole W. Simons Noam D. Beckmann Raimonds Rešcenko Vita Rovite Peculis Raitis Monta Briviba Janis Klovinš Laura Ansone Lorenzo Miano Flora Peyvandi Francesco Malvestiti Nicola Montano Alessandra Bandera Francesco Bruno Arturo Blasi Jacqueline M. Lane Jakob M. Cherry Ryan K. Chung Laura Carvajal-Silva Kevin Aguilar-Valdés Leslie C. Cerpa Tamara V. Arévalo Eduardo Lamoza Alicia Colombo Ricardo A. Verdugo Erika Frangione Xu Xinyi Jennifer Taher Jordan Lerner-Ellis Lindsay Guare Shefali S. Verma Ekaterina Vergasova Michil Trofimov Layal Shaheen Nikolay Plotnikov Anna Kim Dmitrii Kharitonov Alexei Kamelin Alexander Rakitko Eirini Marouli Pasquale Striano Ummu Afeera Zainulabid Ashutosh Kumar Hajar Fa Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Department of Human Genetics McGill University Montreal Quebec Canada Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal Quebec Canada Kyoto-McGill International Collaborative Program in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan Research Fellow Japan Society for the Promotion of Science Tokyo Japan Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Public Health University of Helsinki Helsinki Finland Broad Institute Cambridge MA USA Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Twin Research King’s College London London UK 5 Prime Sciences Inc Montreal Quebec Canada Department of Epidemiology Biostatistics and Occupational Health McGill University Montreal Quebec Canada Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Departmen

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association ofFOXP4with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4’s role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

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Correction: How Can Artificial Intelligence Make Medicine More Preemptive?

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Journal of medical Internet research 2020年第8期22卷 e23645页

作者： Usman Iqbal Leo Anthony Celi Yu-Chuan Jack Li Master Program in Global Health & Development PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan. International Center for Health Information Technology Taipei Medical University Taipei Taiwan. Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States. Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center Boston MA United States. Department of Biostatistics Harvard TH Chan School of Public Health Harvard University Boston MA United States. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Dermatology Taipei Municipal Wan-Fang Hospital Taipei Taiwan. International Medical Informatics Association Geneva Switzerland.

[This corrects the article DOI: .].

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EVALUATION OF SOFTWARE IMPACT DESIGNED FOR biomedical RESEARCH: ARE WE MEASURING WHAT'S MEANINGFUL?

arXiv

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arXiv 2023年

作者： Afiaz, Awan Ivanov, Andrey A. Chamberlin, John Hanauer, David Savonen, Candace L. Goldman, Mary J. Morgan, Martin Reich, Michael Getka, Alexander Holmes, Aaron Pati, Sarthak Knight, Dan Boutros, Paul C. Bakas, Spyridon Caporaso, J. Gregory Del Fiol, Guilherme Hochheiser, Harry Haas, Brian Schloss, Patrick D. Eddy, James A. Albrecht, Jake Fedorov, Andrey Waldron, Levi Hoffman, Ava M. Bradshaw, Richard L. Leek, Jeffrey T. Wright, Carrie Department of Biostatistics University of Washington SeattleWA United States Biostatistics Program Public Health Sciences Division Fred Hutchinson Cancer Center SeattleWA United States Department of Pharmacology and Chemical Biology Emory University School of Medicine Emory University AtlantaGA United States Department of Biomedical Informatics University of Utah Salt Lake CityUT United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California Santa Cruz Santa CruzCA United States Roswell Park Comprehensive Cancer Center BuffaloNY United States University of California San Diego La Jolla CA United States University of Pennsylvania PhiladelphiaPA United States Jonsson Comprehensive Cancer Center University of California Los AngelesCA United States Institute for Precision Health University of California Los AngelesCA United States Department of Human Genetics University of California Los AngelesCA United States Department of Urology University of California Los AngelesCA United States Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Methods Development Laboratory Broad Institute CambridgeMA United States Department of Microbiology and Immunology University of Michigan Ann ArborMI United States Sage Bionetworks SeattleWA United States Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Epidemiology and Biostatistics City University of New York Graduate School of Public Health and Health Policy New YorkNY United States

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that su

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Author Correction: A shared disease-associated oligodendrocyte signature among multiple CNS pathologies

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Nature neuroscience 2023年 2023 Jan 31页

作者： Mor Kenigsbuch Pierre Bost Shahar Halevi Yuzhou Chang Shuo Chen Qin Ma Renana Hajbi Benno Schwikowski Bernd Bodenmiller Hongjun Fu Michal Schwartz Ido Amit Department of Neurobiology Weizmann Institute of Science Rehovot Israel. Department of Immunology Weizmann Institute of Science Rehovot Israel. Systems Biology Group Center for Bioinformatics Biostatistics and Integrative Biology Institut Pasteur CNRS Paris France. Sorbonne Université Complexité du vivant Paris France. University of Zurich Zurich Switzerland. Department of Biomedical Informatics College of Medicine Ohio State University Columbus OH USA. Biomedical Sciences Graduate Program Ohio State University Columbus OH USA. Department of Neuroscience College of Medicine Ohio State University Columbus OH USA. Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel. Department of Neurobiology Weizmann Institute of Science Rehovot Israel. michal.schwartz@weizmann.ac.il. Department of Immunology Weizmann Institute of Science Rehovot Israel. ido.amit@weizmann.ac.il.

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Transient stabilization, rather than inhibition, of MYC amplifies extrinsic apoptosis and therapeutic responses in refractory B-cell lymphoma (vol 33, pg 2429, 2019)

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LEUKEMIA 2020年第5期34卷 1485-1485页

作者： Harrington, Colleen T. Sotillo, Elena Robert, Aude Hayer, Katharina E. Bogusz, Agata M. Psathas, James Yu, Duonan Taylor, Deanne Dang, Chi V. Klein, Peter S. Hogarty, Michael D. Geoerger, Birgit El-Deiry, Wafik S. Wiels, Joelle Thomas-Tikhonenko, Andrei Division of Cancer Pathobiology Children’s Hospital of Philadelphia Philadelphia USA Cell & Molecular Biology Graduate Group Perelman School of Medicine at the University of Pennsylvania Philadelphia USA Stanford Cancer Institute Stanford USA CNRS UMR 8126 Univ Paris-Sud - Université Paris-Saclay Institut Gustave Roussy Villejuif France Department of Biomedical and Health Informatics Children’s Hospital of Philadelphia Philadelphia USA Department of Pathology and Laboratory Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia USA The Janssen Pharmaceutical Companies of Johnson & Johnson Malvern USA Noncoding RNA Center Yangzhou University Yangzhou China Department of Pediatrics Perelman School of Medicine at the University of Pennsylvania Philadelphia USA Molecular and Cellular Oncogenesis Program The Wistar Institute Philadelphia USA Department of Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia USA Division of Oncology Children’s Hospital of Philadelphia Philadelphia USA CNRS UMR 8203 Univ Paris-Sud - Université Paris-Saclay Institut Gustave Roussy Villejuif France Department of Pediatric and Adolescent Oncology Univ Paris-Sud - Université Paris-Saclay Institut Gustave Roussy Villejuif France Department of Pathology and Laboratory Medicine Brown University Medical School Providence USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Author Correction: Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease

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Nature communications 2023年第1期14卷 4370页

作者： Matteo D'Antonio Jennifer P Nguyen Timothy D Arthur Hiroko Matsui Agnieszka D'Antonio-Chronowska Kelly A Frazer Department of Pediatrics University of California San Diego La Jolla CA 92093 USA. madantonio@ucsd.edu. Division of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA. madantonio@ucsd.edu. Institute of Genomic Medicine University of California San Diego 9500 Gilman Dr La Jolla CA 92093 USA. madantonio@ucsd.edu. Division of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA. Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA. Biomedical Sciences Graduate Program University of California San Diego La Jolla CA 92093 USA. Institute of Genomic Medicine University of California San Diego 9500 Gilman Dr La Jolla CA 92093 USA. Department of Pediatrics University of California San Diego La Jolla CA 92093 USA. Department of Pediatrics University of California San Diego La Jolla CA 92093 USA. kafrazer@health.ucsd.edu. Institute of Genomic Medicine University of California San Diego 9500 Gilman Dr La Jolla CA 92093 USA. kafrazer@health.ucsd.edu.

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biomedical discovery through the integrative biomedical knowledge hub (iBKH)

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iScience 2023年第4期26卷 106460页

作者： Su, Chang Hou, Yu Zhou, Manqi Rajendran, Suraj Maasch, Jacqueline R.M. A. Abedi, Zehra Zhang, Haotan Bai, Zilong Cuturrufo, Anthony Guo, Winston Chaudhry, Fayzan F. Ghahramani, Gregory Tang, Jian Cheng, Feixiong Li, Yue Zhang, Rui DeKosky, Steven T. Bian, Jiang Wang, Fei Department of Health Service Administration and Policy College of Public Health Temple University Philadelphia 19122 PA United States Department of Population Health Sciences Weill Cornell Medicine New York 10065 NY United States Department of Surgery University of Minnesota Minneapolis 55455 MN United States Department of Computational Biology Cornell University Ithaca 14850 NY United States Tri-Institutional Computational Biology & Medicine Program Cornell University New York 10065 NY United States Department of Computer Science Cornell Tech New York 10044 NY United States Department of Physiology and Biophysics Weill Cornell Medicine New York 10065 NY United States Computer Science Cornell University Ithaca 14850 NY United States Department of Medicine Weill Cornell Medicine New York 10021 NY United States Mila-Quebec AI Institute and HEC Montreal Montreal H2S 3H1 QC Canada Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland 44195 OH United States Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland 44195 OH United States Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland 44106 OH United States School of Computer Science McGill University Montreal H3A 0C6 QC Canada Department of Neurology College of Medicine University of Florida Gainesville 32610 FL United States Department of Health Outcomes & Biomedical Informatics College of Medicine University of Florida Gainesville 32610 FL United States

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available. © 2023 The Authors

关键词： Bioinformatics Drugs Systems biology

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SUBCELLULAR TO TISSUE SCALE MODELING OF ISCHEMIA AND SPREADING DEPOLARIZATION

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IBRO Neuroscience Reports 2023年 15卷 S792-S792页

作者： Adam Newton Craig Kelley Amy Guo Joy Wang Sydney Zink Marcello Distasio Robert A Mcdougal William Lytton SUNY Downstate Health Sciences University Physiology And Pharamcology New York United States of America SUNY Downstate Health Sciences University Program In Biomedical Engineering New York United States of America Yale School of Public Health Health Informatics Program NEW HAVEN United States of America Yale School of Medicine Department Of Pathology NEW HAVEN United States of America Yale University Department Of Biostatistics NEW HAVEN United States of America Yale University Center For Medical Informatics NEW HAVEN United States of America Yale University Program In Computational Biology And Bioinformatic NEW HAVEN United States of America Yale University Wu Tsai Institute NEW HAVEN United States of America SUNY Downstate Health Sciences University Department Of Neurology New York United States of America Kings County Hospital Center Department Of Neurology New York United States of America SUNY Downstate Health Sciences University The Robert F. Furchgott Center Neural And Behavioral Science New York United States of America

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Correction: Profiling chromatin accessibility in pediatric acute lymphoblastic leukemia identifies subtype-specific chromatin landscapes and gene regulatory networks

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Leukemia 2024年第8期38卷 1867页

作者： Jonathan D Diedrich Qian Dong Daniel C Ferguson Brennan P Bergeron Robert J Autry Maoxiang Qian Wenjian Yang Colton Smith James B Papizan Jon P Connelly Kohei Hagiwara Kristine R Crews Shondra M Pruett-Miller Ching-Hon Pui Jun J Yang Mary V Relling William E Evans Daniel Savic Hematological Malignancies Program and Center for Precision Medicine in Leukemia St. Jude Children's Research Hospital Memphis TN USA. Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN USA. Graduate School of Biomedical Sciences St. Jude Children's Research Hospital Memphis TN USA. Integrated Biomedical Sciences Program University of Tennessee Health Science Center Memphis TN USA. Department of Cell and Molecular biology and Center for Advanced Genome Engineering St. Jude Children's Research Hospital Memphis TN USA. Department of Computational Biology St. Jude Children's Research Hospital Memphis TN USA. Department of Oncology St. Jude Children's Research Hospital Memphis TN USA. Department of Pathology St. Jude Children's Research Hospital Memphis TN USA. Hematological Malignancies Program and Center for Precision Medicine in Leukemia St. Jude Children's Research Hospital Memphis TN USA. daniel.savic@***. Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN USA. daniel.savic@***. Integrated Biomedical Sciences Program University of Tennessee Health Science Center Memphis TN USA. daniel.savic@***.

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