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arXiv 2018年

作者： Fanny Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Author Correction: General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

npj Women's Health

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npj Women's Health 2024年第1期2卷 1页

作者： Jamison H. Burks Patrick Kasl Benjamin L. Smarr Lauryn Keeler Bruce Severine Soltani Varun Viswanath Wendy Hartogensis Frederick M. Hecht Ashley E. Mason Stephan Dilchert Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Shu Chien-Gene Lay Department of Bioengineering University of California San Diego San Diego CA USA Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA UC San Diego Health Department of Biomedical Informatics University of California San Diego San Diego CA USA Bioinformatics and Systems Biology University of California San Diego San Diego CA USA Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA Osher Center for Integrative Health University of California San Francisco San Francisco CA USA Department of Management Zicklin School of Business Baruch College The City University of New York New York NY USA San Diego Supercomputer Center University of California San Diego San Diego CA USA

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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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General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

npj Women's Health

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npj Women's Health 2024年第1期2卷 1-11页

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.

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Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

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Journal of Medical Virology 2024年第5期96卷 e29648-e29648页

作者： Chen, Sheng-Yin Hsieh, Tina Yi Jin Hung, Yao-Min Oh, Jae Won Chen, Shen-Kai Wang, Shiow-Ing Chang, Renin Wei, James Cheng-Chung Clinical and Translational Epidemiology Unit Massachusetts General Hospital and Harvard Medical School Boston Morocco Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston Morocco Department of Epidemiology Harvard T. H. Chan School of Public Health Boston Morocco Department of Obstetrics & Gynecology Beth Israel Deaconess Medical Center Boston Morocco Department of Bioinformatics Harvard Medical School Boston Morocco Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Neurology Brigham and Women's Hospital Boston Morocco Department of Neurology Harvard Medical School Boston MA United States Department of Education Kaohsiung Chang Gung Memorial Hospital Boston MA United States Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Department of Nursing Jen-Teh Junior College of Medicine Nursing and Management Miaoli County Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Recreation and Sports Management Tajen University Pintung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Office of Research and Development Asia University Taichung Taiwan

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56–0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48–0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53–0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42–0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23–0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73–0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50–0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke even

关键词： cohort study COVID-19 vaccination strokes

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Understanding Social Inequalities in Childhood Asthma: Quantifying the Mediating Role of Modifiable Early-Life Risk Factors in Seven European Birth Cohorts

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Journal of Allergy and Clinical Immunology: In Practice 2025年第6期13卷 1385-1396页

作者： Pinot de Moira, Angela Aurup, Anne V. Avraam, Demetris Zugna, Daniela Jensen, Aksel Karl Georg Welten, Marieke Cadman, Timothy de Lauzon-Guillain, Blandine Duijts, Liesbeth Elhakeem, Ahmed Esplugues, Ana Garcia-Aymerich, Judith García-Baquero, Gonzalo González Safont, Llúcia Harris, Jennifer R. Iñiguez, Carmen Jaddoe, Vincent W.V. McEachan, Rosemary R.C. Nader, Johanna L.T. Santa Marina, Loreto Swertz, Morris A. Tafflet, Muriel Vrijheid, Martine Wright, John Yang, Tiffany C. Taylor-Robinson, David Richiardi, Lorenzo Nybo Andersen, Anne-Marie School of Public Health Imperial College London London United Kingdom Department of Public Health University of Copenhagen Copenhagen Denmark Department of Nutrition Exercise and Sports University of Copenhagen Copenhagen Denmark Department of Public Health Policy and Systems University of Liverpool Liverpool United Kingdom Cancer Epidemiology Unit Department of Medical Sciences University of Turin Turin Italy Department of Pediatrics Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Generation R Study Group Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Genomics Coordination Center University Medical Center Groningen University of Groningen Groningen Netherlands Department of Genetics University Medical Center Groningen University of Groningen Groningen Netherlands Université Paris Cité and Université Sorbonne Paris Nord INSERM INRAE Centre for Research in Epidemiology and Statistics Paris France Population Health Science Bristol Medical School Bristol United Kingdom MRC Integrative Epidemiology Unit at the University of Bristol Bristol United Kingdom Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP) Madrid Spain Department of Nursing University of Valencia Valencia Spain Epidemiology and Environmental Health Joint Research Unit the Foundation for the Promotion of Health and Biomedical Research of Valencia Region Universitat Jaume I-Universitat de València Valencia Spain Barcelona Institute for Global Health (ISGlobal) Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain CEADIR Faculty of Biology University of Salamanca Campus Miguel de Unamuno Avda Licenciado Méndez Nieto s/n Salamanca Spain Biodonostia Environmental Epidemiology and Child Development Group San Sebastian Spain Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway Department of Statistics and Operational Research Universitat de València València Spain Bradford Institute f

Background: Children growing up in disadvantaged socioeconomic circumstances (SECs) have an increased risk of asthma. Objective: To increase our understanding of the pathways to inequalities in asthma and potential targets for intervention by (1) examining how the social patterning of asthma and its early-life risk factors varies across countries and (2) quantifying the mediation of observed inequalities by early-life risk factors. Methods: We used data for 107,884 mother-child dyads from 7 European birth cohorts across 6 countries. Maternal education was the primary exposure measure of early-life SECs. The outcome was current asthma in childhood (3-12 years). Inequalities were examined using multivariable regression and random effects meta-analysis. The mediating effects of early-life risk factors (maternal smoking during pregnancy, adverse birth outcomes, and breastfeeding duration) were examined using counterfactual mediation analysis. Results: In meta-analysis, children of mothers with low/medium versus high education had a 17% (95% confidence interval: 8%-27%, I² = 21.6%) increased risk of asthma. Cohort-specific risk ratios ranged between 1.07 (0.97-1.18, Danish National Birth Cohort, Denmark) and 1.61 (1.08-2.40, study on the pre- & early postnatal determinants of child health & development, France). The early-life risk factors were similarly socially patterned, but with greater heterogeneity across cohorts (I² range = 66.2%-95.3%). The mediation analysis suggested that these factors play a relevant role in mediating observed inequalities (proportion mediated range: 0.08-0.72). Conclusions: There was a consistent tendency for children from disadvantaged SECs to be at greater risk of asthma in the European cohorts examined. Our results suggest that early-life risk factors partially mediate these disparities and, therefore, that public health interventions in the perinatal period may help to address inequalities in asthma. © 2025 The Autho

关键词： Birth cohort Childhood asthma Cross-cohort Inequalities Meta-analysis Socioeconomic

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Corrigendum to “Inhibition of colorectal cancer targets IL-6, CTLA-4, & B7-2 by Tislelizumab: Molecular docking, dynamics, & STRING protein-protein network analysis” [Inform Med Unlocked 41 (2023) 101323]

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Informatics in Medicine Unlocked 2023年 43卷

作者： Mahmoud Elkazzaz Mahmoud Abdalrahman Shahid Ullah Israa M. Shamkh Amr Ahmed Ted X. Wu Maii S. Elsharayidi Mohammad Shahbaz Khan Aziz-ur Rehman Marwa M. Lotfy Abdullah Haikal Tianshun Gao Department of Chemistry and Biochemistry Faculty of Science Damietta University Damietta 7952567 Egypt Science Department Milton Academy Milton MA 02186 USA Lowell Institute CPS Northeastern University 360 Huntington Ave Boston MA 02115 USA S Khan Lab Mardan Khyber Pakhtunkhwa Pakistan Chemo and Bioinformatics Lab Bio Search Research Institution BSRI Giza 12613 Egypt Public Health Department First Health Cluster Ministry of Health Riyadh 966-11 Saudi Arabia Central Public Health Laboratories Egyptian Ministry of Health Egypt Children's National Hospital Washington DC USA Keystone Pharmacogenomics LLC Bensalem PA 19020 USA Faculty of Pharmacy Zagazig University Zagazig 44511 Egypt Department of Pharmacognosy Faculty of Pharmacy Mansoura University Mansoura 35516 Egypt Big Data Center The Seventh Affiliated Hospital of Sun Yat-sen University Shenzhen 518107 PR China

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Workshop proceedings: GWAS summary statistics standards and sharing

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Cell Genomics 2021年第1期1卷 100004-100004页

作者： MacArthur, Jacqueline A.L. Buniello, Annalisa Harris, Laura W. Hayhurst, James McMahon, Aoife Sollis, Elliot Cerezo, Maria Hall, Peggy Lewis, Elizabeth Whetzel, Patricia L. Bahcall, Orli G. Barroso, Inês Carroll, Robert J. Inouye, Michael Manolio, Teri A. Rich, Stephen S. Hindorff, Lucia A. Wiley, Ken Parkinson, Helen European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton United Kingdom BHF Data Science Centre Health Data Research UK London United Kingdom Division of Genomic Medicine National Human Genome Research Institute National Institutes of Health Bethesda 20892 MD United States Cell Genomics Cell Press 50 Hampshire St. 5th Floor Cambridge 02139 MA United States Exeter Centre of Excellence in Diabetes (EXCEED) University of Exeter Medical School Exeter United Kingdom Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN United States Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge CB1 8RN United Kingdom Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute 75 Commercial Rd. Melbourne 3004 VIC Australia The Alan Turing Institute London United Kingdom Center for Public Health Genomics University of Virginia Charlottesville 22908 VA United States

Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine. © 2022 The Authors

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