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Correction to: Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

Microbiome 2018年第1期6卷 212页

作者： Justin D Silverman Heather K Durand Rachael J Bloom Sayan Mukherjee Lawrence A David Program in Computational Biology and Bioinformatics Duke University CIEMAS Room 2171 101 Science Drive Box 3382 Durham NC 27708 USA. Medical Scientist Training Program Duke University Durham NC 27708 USA. Center for Genomic and Computational Biology Duke University Durham NC 27708 USA. Department of Molecular Genetics and Microbiology Duke University Durham NC 27708 USA. University Program in Genetics and Genomics Duke University Durham NC 27708 USA. Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC 27708 USA. Program in Computational Biology and Bioinformatics Duke University CIEMAS Room 2171 101 Science Drive Box 3382 Durham NC 27708 USA. lawrence.david@duke.edu. Center for Genomic and Computational Biology Duke University Durham NC 27708 USA. lawrence.david@duke.edu. University Program in Genetics and Genomics Duke University Durham NC 27708 USA. lawrence.david@duke.edu. Department of Molecular Genetics and Microbiology Duke University Durham NC 27708 USA. lawrence.david@duke.edu.

AbstractFollowing publication of the original article [1], the authors noticed an error in the presentation of equations in the PDF version.

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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1006-LBP

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Human Immunology 2014年第6期75卷 481-481页

作者： Mack, Steven J. Yamaguchi, Ken D. Salamon, Hugh Pena, Anthony G. Horton, Robert M. Francis-Lyon, Patricia Maiers, Martin Hollenbach, Jill A. Center for Genetics Children’s Hospital Oakland Research Institute Oakland CA 94609 United States Knowledge Synthesis Inc. Berkeley CA 94710 United States Department of Computer Science University of San Francisco San Francisco CA 94117 United States Department of Microbiology and Immunology University of California San Francisco San Francisco CA 94143 United States Division of Bioinformatics National Marrow Donor Program Minneapolis MN 55413 United States

Aim Analyses of highly polymorphic HLA and KIR locus data require specialized tools and methods, but most modern analytical programs are tailored for SNPs. A typical analytical workflow for HLA and KIR data requires trafficking data between several programs, which is time intensive, error prone and limits reproducibility. The IDAWG has been developing an integrated data-management and analysis sy stem tailored to these important genetic systems. Methods We have developed the Toolkit for Immunogenomic Data Exchange and Storage (TIDES) and Bridging ImmunoGenomic Data-Analysis Workflow Gaps (BIGDAWG) systems to address the unmet need for consistent HLA and KIR data management and analysis. TIDES is a free, open-source system that converts HLA and KIR genotypes derived from widely used genotyping platforms into Genotype List (GL) Strings, which are registered with the NMDP’s GL Service (***). BIGDAWG is an automated pipeline, scripted in R, that performs common data analyses of multi-locus highly polymorphic genetic data characteristic of HLA and KIR genes. Results TIDES stores GL String-encoded data with project-related data, and exports these to PED and POP data-analysis formats. TIDES can be deployed on an AWS EC2 or equivalent Linux environment, on a local network or on a standalone machine, per user preference. A prototype TIDES implementation is deployed at ***. Starting with unambiguous multi-locus genotype data for case-control groups, BIGDAWG estimates user-specified haplotypes, bins low-frequency haplotypes to enable chi-squared testing, calculates odds-ratios, confidence intervals and p-values for each haplotype, and generates figures and tables for each comparison. Conclusions The integration of TIDES and BIGDAWG will create a workflow that accepts ambiguous genotype data and returns case-control analysis results. This approach streamlines data analysis and allows the consistent storage and exchange of HLA and KIR genot

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Author Correction: Effect of de novo transcriptome assembly on transcript quantification

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Scientific reports 2019年第1期9卷 18041页

作者： Ping-Han Hsieh Yen-Jen Oyang Chien-Yu Chen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan. Department of Bio-Industrial Mechatronics Engineering National Taiwan University Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw. Genome and Systems Biology Program National Taiwan University and Academia sinica Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw.

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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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Proceedings of the 15th Annual UT-KBRIN bioinformatics Summit 2016 : Cadiz, KY, USA. 8-10 April 2016

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BMC bioinformatics 2016年第SUPPL 10期17卷 297页

作者： Eric C. Rouchka Julia H. Chariker Benjamin J. Harrison Juw Won Park Xueyuan Cao Stanley Pounds Susana Raimondi James Downing Raul Ribeiro Jeffery Rubnitz Jatinder Lamba Bernie J. Daigle Deborah Burgess Stephanie Gehrlich John C. Carmen Nicholas Johnson Chandrakanth Emani Kalpani De Silva Michael P. Heaton Theodore S. Kalbfleisch Teeradache Viangteeravat Rahul Mudunuri Oluwaseun Ajayi Fatih Şen Eunice Y. Huang Mohammad Mohebbi Luaire Florian Douglas J. Jackson John F. Naber AKM Sabbir Sally R. Ellingson Yuping Lu Charles A Phillips Michael A. Langston Rahul K. Sevakula Raghuveer Thirukovalluru Nishchal K. Verma Yan Cui Mohammed Sayed Jing Wang Qi Liu Yu Shyr Xiaofei Zhang Naresh Prodduturi Gavin R. Oliver Diane Grill Jie Na Jeanette Eckel-Passow Eric W. Klee Michael M. Goodin Mark Farman Harrison Inocencio Chanyong Jang Jerzy W. Jaromczyk Neil Moore Kelly Sovacool Leon Dent Mike Izban Sammed Mandape Shruti Sakhare Siddharth Pratap Dana Marshall M Scotty DePriest James N. MacLeod Hanady Adam Ethan Blandford Joel Campbell Joshua Castlen Brittany Dixon Ginger Gilbert Aaron Hall Philip Kreisle Jessica Lasher Bethany Oakes Allison Speer Maximilian Valentine Naga Satya V. Rao Nagisetty Rony Jose Robert Rooney David Hains Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core Louisville USA Department of Psychological and Brain Sciences University of Louisville Louisville USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville USA Department of Biostatistics St. Jude Children’s Research Hospital Memphis USA Department of Pathology St. Jude Children’s Research Hospital Memphis USA Department of Oncology St. Jude Children’s Research Hospital Memphis USA Department of Pharmacotherapy and Translational Research College of Pharmacy University of Florida Gainesville USA Department of Biological Sciences University of Memphis Memphis USA Department of Computer Science University of Memphis Memphis USA Department of Computer Science Northern Kentucky University Highland Heights USA Department of Biological Sciences Northern Kentucky University Highland Heights USA Department of Biology Western Kentucky University Bowling Green USA Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville USA United States Department of Agriculture Agricultural Research Service United States Meat Animal Research Center Clay Center USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville USA Biomedical Informatics Core Children’s Foundation Research Institute Memphis USA Department of Pediatrics University of Tennessee Health Science Center Memphis USA Department of Electrical and Computer Engineering University of Louisville Louisville USA Department of Computer Science University of Kentucky Lexington USA Division of Biomedical Informatics College of Medicine University of Kentucky Lexington USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville USA Department of Electrical E

I1 Proceedings of the Fifteenth Annual UT- KBRIN bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large scale discovery of active enhancers from nasce

关键词： True Negative Differential Expression UCSC Genome Browser Laboratory Information Management System Biomedical Informatics

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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Development and evaluation of a hospital-wide healthcare-associated surgical site infection detection algorithm

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Journal of Microbiology, Immunology and Infection 2015年第2期48卷 S43-S43页

作者： Yi-Ju Tseng Bo-Chiang Huang Hui-Chi Lin Ming-Yuan Chen Rung-Ji Shang Wang-Huei Sheng Yee-Chun Chen Feipei Lai Shan-Chwen Chang Children's Hospital Informatics Program Boston Children's Hospital Boston MA USA Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan Center for Infection Control National Taiwan University Hospital (NTUH) Taiwan Information Systems Office NTUH Taipei Taiwan Department of Internal Medicine NTUH and College of Medicine Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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