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The need to implement FAIR principles in biomolecular simulations

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

Generalized Spatio Temporal Autoregressive Rainfall-Enso Pattern in East Java Indonesia 1

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Generalized Spatio Temporal Autoregressive Rainfall-Enso Pat...

1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Caraka, Rezzy Eko Ulhusna, Mishbah Darmawan Supatmanto, Budi Goldameir, Noor Ell Hutapea, Bonar Darmawan, Gumgum Novitasari, Dian Candra Rini Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia School of Mathematical Sciences National University of Malaysia Malaysia Department of Statistics Padjadjaran University Indonesia Modifikasi Cuaca - BPPT Balai Besar Teknologi Indonesia Faculty of Psychology Tarumanagara University Indonesia Department of Mathematics UIN Sunan Ampel Surabaya Indonesia Riau University Indonesia 'Yptk' Padang Universitas Putra Indonesia Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538694220

Monthly rainfall analysis and forecasts are made to be able to provide a clear picture of the rain and climate conditions that have occurred and will occur in the territory of Indonesia. The information can be known in an area that has the most substantial maximum rainfall and in an area which has the lowest rainfall described and presented spatial pattern by using the ArcView-GIS. The purpose of making monthly rainfall forecasts is to be used and utilized by users, especially in the agriculture and irrigation sectors to plan and make decisions within the next 1 to 3 months related to climatic conditions, especially rainfall. In this paper, we use annual rainfall data in six location East Java. We analysis ENSO phenomena as well as rainfall forecasting in January - March 2018 by using generalized space-time autoregressive and get an accuracy MAPE out samp;e amount 2.95% dan RMSE out sample amount 4.77. © 2018 IEEE.

关键词： Rain

Author Correction: A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome

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Nature communications 2022年第1期13卷 7538页

作者： Poonam Chitale Alexander D Lemenze Emily C Fogarty Avi Shah Courtney Grady Aubrey R Odom-Mabey W Evan Johnson Jason H Yang A Murat Eren Roland Brosch Pradeep Kumar David Alland Ray V. Lourenco Center for the Study of Emerging and Re-emerging Pathogens Rutgers University-New Jersey Medical School Newark NJ USA. Public Health Research Institute Rutgers University-New Jersey Medical School Newark NJ USA. Department of Pathology Immunology and Laboratory Medicine New Jersey Medical School Rutgers-The State University of New Jersey Newark NJ USA. Department of Medicine University of Chicago Chicago IL USA. Committee on Microbiology University of Chicago Chicago IL USA. Department of Microbiology Biochemistry and Molecular Genetics Rutgers University-New Jersey Medical School Newark NJ USA. Division of Computational Biomedicine Boston University School of Medicine and Bioinformatics Program Boston University Boston MA USA. Bioinformatics Program Boston University Boston MA USA. Center for Data Science Rutgers University-New Jersey Medical School Newark NJ USA. Helmholtz Institute for Functional Marine Biodiversity (HIFMB) Oldenburg Germany. Bay Paul Center Marine Biological Laboratory Woods Hole MA USA. Institut Pasteur Université Paris Cité Unit for Integrated Mycobacterial Pathogenomics CNRS UMR 6047 Paris France. Ray V. Lourenco Center for the Study of Emerging and Re-emerging Pathogens Rutgers University-New Jersey Medical School Newark NJ USA. allandda@njms.rutgers.edu. Public Health Research Institute Rutgers University-New Jersey Medical School Newark NJ USA. allandda@njms.rutgers.edu.

Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

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Nature genetics 2023年第6期55卷 1080页

作者： Bernardo Rodriguez-Martin Eva G Alvarez Adrian Baez-Ortega Jorge Zamora Fran Supek Jonas Demeulemeester Martin Santamarina Young Seok Ju Javier Temes Daniel Garcia-Souto Harald Detering Yilong Li Jorge Rodriguez-Castro Ana Dueso-Barroso Alicia L Bruzos Stefan C Dentro Miguel G Blanco Gianmarco Contino Daniel Ardeljan Marta Tojo Nicola D Roberts Sonia Zumalave Paul A Edwards Joachim Weischenfeldt Montserrat Puiggròs Zechen Chong Ken Chen Eunjung Alice Lee Jeremiah A Wala Keiran M Raine Adam Butler Sebastian M Waszak Fabio C P Navarro Steven E Schumacher Jean Monlong Francesco Maura Niccolo Bolli Guillaume Bourque Mark Gerstein Peter J Park David C Wedge Rameen Beroukhim David Torrents Jan O Korbel Iñigo Martincorena Rebecca C Fitzgerald Peter Van Loo Haig H Kazazian Kathleen H Burns Peter J Campbell Jose M C Tubio Department of Zoology Genetics and Physical Anthropology Universidade de Santiago de Compostela Santiago de Compostela Spain. Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain. Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Transmissible Cancer Group Department of Veterinary Medicine University of Cambridge Cambridge UK. The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Genome Data Science Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology (BIST) Barcelona Spain. Institucio Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. The Francis Crick Institute London UK. Department of Human Genetics University of Leuven Leuven Belgium. Genomes and Disease Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea. Cancer Ageing and Somatic Mutation Programme Wellcome Sanger Institute Cambridge UK. Department of Biochemistry Genetics and Immunology University of Vigo Vigo Spain. Galicia Sur Health Research Institute Vigo Spain. Faculty of Science and Technology University of Vic-Central University of Catalonia (UVic-UCC) Vic Spain. Barcelona Supercomputing Center (BSC) Barcelona Spain. Experimental Cancer Genetics Wellcome Sanger Institute Cambridge UK. Oxford Big Data Institute University of Oxford Oxford UK. DNA Repair and Genome Integrity Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Department of Biochemistry and Molecular Biology Universidade de Santiago de Compostela Santiago de Compostela Spain. Medical R

Generalized fitch graphs II: Sets of binary relations that are explained by edge-labeled trees

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arXiv 2019年

作者： Hellmuth, Marc Seemann, Carsten R. Stadler, Peter F. Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Strasse 47 Greifswald D-17487 Germany Saarland University Center for Bioinformatics Building E 2.1 P.O. Box 151150 Saarbrücken D-66041 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig D-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 Leipzig D-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine University of Leipzig Härtelstraße 16-18 Leipzig D-04107 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 Wien A-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa Fe NM 87501 United States

Fitch graphs G = (X, E) are digraphs that are explained by {0/, 1}-edge-labeled rooted trees T with leaf set X: there is an arc (x, y) ∈ E if and only if the unique path in T that connects the last common ancestor lca(x, y) of x and y with y contains at least one edge with label "1". In practice, Fitch graphs represent xenology relations, i.e., pairs of genes x and y for which a horizontal gene transfer happened along the path from lca(x, y) to y. In this contribution, we generalize the concept of Fitch graphs and consider trees T that are equipped with edge-labeling λ : E → P(M) that assigns to each edge a subset M' ⊆ M of colors. Given such a tree, we can derive a map Ε(T,λ) (or equivalently a set of not necessarily disjoint binary relations), such that i ∈ Ε(T,λ)(x, y) (or equivalently (x, y) ∈ Ri) with x, y ∈ X, if and only if there is at least one edge with color i from lca(x, y) to y. The central question considered here: Is a given map Ε a Fitch map, i.e., is there there an edge-labeled tree (T, λ) with Ε(T,λ) = Ε, and thus explains Ε? Here, we provide a characterization of Fitch maps in terms of certain neighborhoods and forbidden submaps. Further restrictions of Fitch maps are considered. Moreover, we show that the least-resolved tree explaining a Fitch map is unique (up to isomorphism). In addition, we provide a polynomial-time algorithm to decide whether Ε is a Fitch map and, in the affirmative case, to construct the (up to isomorphism) unique least-resolved tree (T∗, λ∗) that explains Ε.MSC Codes 68R01, 05C05, 92D15 Copyright © 2019, The Authors. All rights reserved.

关键词： Phylogenetic

STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability

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iMetaOmics

iMetaOmics 2024年第1期1卷

作者： Jinghong Chen Lingxuan Zhu Weiming Mou Anqi Lin Dongqiang Zeng Chang Qi Zaoqu Liu Aimin Jiang Bufu Tang Wenjie Shi Ulf D. Kahlert Jianguo Zhou Shipeng Guo Xiaofan Lu Xu Sun Trunghieu Ngo Zhongji Pu Baolei Jia Che Ok Jeon Yongbin He Haiyang Wu Shuqin Gu Wisit Cheungpasitporn Haojie Huang Weipu Mao Shixiang Wang Xin Chen Loïc Cabannes Gerald Sng Gui Ren Iain S. Whitaker Stephen Ali Quan Cheng Kai Miao Shuofeng Yuan Peng Luo Department of Oncology Zhujiang Hospital Southern Medical University Guangzhou China The Second School of Clinical Medicine Southern Medical University Guangzhou China Department of Urology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Oncology Nanfang Hospital Southern Medical University Guangzhou China Institute of Logic and Computation TU Wien Wien Austria Institute of Basic Medical Sciences Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Department of Urology Changhai Hospital Naval Medical University (Second Military Medical University) Shanghai China Department of Radiation Oncology Zhongshan Hospital Fudan University Shanghai China Molecular and Experimental Surgery University Clinic for General- Visceral- Vascular- and Trans-Plantation Surgery Medical Faculty University Hospital Magdeburg Otto-von Guericke University Magdeburg Germany Department of Oncology The Second Affiliated Hospital of Zunyi Medical University Zunyi China Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Comprehensive Cancer Center Erlangen-EMN Erlangen Germany GZDLab Chongqing China Department of Cancer and Functional Genomics Institute of Genetics and Molecular and Cellular Biology CNRS/INSERM/UNISTRA Illkirch France Linguistique Informatique UFR-Linguistique Université Paris Cité Paris France Xianghu Laboratory Hangzhou China Department of Life Science Chung-Ang University Seoul Korea School of Sport Medicine and Rehabilitation Beijing Sport University Beijing China Department of Microbiology and Immunology School of Medicine Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Department of Graduate School Tianjin Medical University Tianjin China Department of Clinical College of Neurology Neurosurgery and Neurorehabilitation Tianjin Medical Universi

Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32-item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit *** . This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32-item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts. It consists of key aspects, including question collection, querying approaches, and assessment techniques. It enhances research quality and facilitates advances in

关键词： generative AI medical and life science contexts reliability standardized assessment guidelines

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

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NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

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arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

Variant interpretation using population databases: lessons from gnomAD

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arXiv 2021年

作者： Gudmundsson, Sanna Singer-Berk, Moriel Watts, Nicholas A. Phu, William Goodrich, Julia K. Solomonson, Matthew Rehm, Heidi L. MacArthur, Daniel G. O'Donnell-Luria, Anne Abreu, Maria Aguilar Salinas, Carlos A. Ahmad, Tariq Albert, Christine M. Alföldi, Jessica Ardissino, Diego Armean, Irina M. Atzmon, Gil Banks, Eric Barnard, John Baxter, Samantha M. Beaugerie, Laurent Benjamin, Emelia J. Benjamin, David Bergelson, Louis Boehnke, Michael Bonnycastle, Lori L. Bottinger, Erwin P. Bowden, Donald W. Bown, Matthew J. Brant, Steven Calvo, Sarah E. Campos, Hannia Chambers, John C. Chan, Juliana C. Chao, Katherine R. Chapman, Sinéad Chasman, Daniel Chisholm, Rex Cho, Judy Chowdhury, Rajiv Chung, Mina K. Chung, Wendy Cibulskis, Kristian Cohen, Bruce Collins, Ryan L. Connolly, Kristen M. Correa, Adolfo Covarrubias, Miguel Cummings, Beryl Dabelea, Dana Daly, Mark J. Danesh, John Darbar, Dawood Denny, Joshua Donnelly, Stacey Duggirala, Ravindranath Dupuis, Josée Ellinor, Patrick T. Elosua, Roberto Emery, James England, Eleina Erdmann, Jeanette Esko, Tõnu Evangelista, Emily Farjoun, Yossi Fatkin, Diane Ferriera, Steven Florez, Jose Francioli, Laurent Franke, Andre Färkkilä, Martti Gabriel, Stacey Garimella, Kiran Gauthier, Laura D. Gentry, Jeff Getz, Gad Glahn, David C. Glaser, Benjamin Glatt, Stephen J. Goldstein, David Gonzalez, Clicerio Goodrich, Julia Groop, Leif Gudmundsson, Sanna Gupta, Namrata Haessly, Andrea Haiman, Christopher Hall, Ira Hanis, Craig Harms, Matthew Hiltunen, Mikko Holi, Matti M. Hultman, Christina M. Jalas, Chaim Jeandet, Thibault Kallela, Mikko Kaplan, Diane Kaprio, Jaakko Karczewski, Konrad J. Kathiresan, Sekar Kenny, Eimear Kim, Bong-Jo Kim, Young Jin Kirov, George Kooner, Jaspal Koskinen, Seppo Krumholz, Harlan M. Kugathasan, Subra Kwak, Soo Heon Laakso, Markku Lake, Nicole Langsford, Trevyn Laricchia, Kristen M. Lehtimäki, Terho Lek, Monkol Lipscomb, Emily Llanwarne, Christopher Loos, Ruth J.F. Lubitz, Steven A. Luna, Teresa Tusie Ma, Ronald C.W. MacArthur, Daniel G. Marcus, Gregory M. Marrugat, Ja University of Miami Miller School of Medicine Gastroenterology Miami United States Unidad de Investigacion de Enfermedades Metabolicas Instituto Nacional de Ciencias Medicas y Nutricion Mexico City Mexico Peninsula College of Medicine and Dentistry Exeter United Kingdom Division of Preventive Medicine Brigham and Women's Hospital BostonMA United States Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Analytic and Translational Genetics Unit Massachusetts General Hospital BostonMA United States Department of Cardiology University Hospital Parma Italy European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge United Kingdom Department of Biology Faculty of Natural Sciences University of Haifa Haifa Israel Department of Medicine and Genetics Albert Einstein College of Medicine BronxNY United States Data Science Platform Broad Institute of MIT and Harvard CambridgeMA United States Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic ClevelandOH United States Sorbonne Université APHP Gastroenterology Department Saint Antoine Hospital Paris France NHLBI Boston University's Framingham Heart Study FraminghamMA United States Department of Medicine Boston University School of Medicine BostonMA United States Department of Epidemiology Boston University School of Public Health BostonMA United States Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann ArborMS United States National Human Genome Research Institute National Institutes of Health BethesdaMD United States The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Biochemistry Wake Forest School of Medicine Winston-SalemNC

Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every human genome, and supports the discovery of new disease-gene relationships. The Genome Aggregation database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://***/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per-base expression levels, constraint scores, and variant co-occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease. © 2021, CC BY.

关键词： Genes