检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Maier-Hein, Lena Eisenmann, Matthias Sarikaya, Duygu März, Keno Collins, Toby Malpani, Anand Fallert, Johannes Feussner, Hubertus Giannarou, Stamatia Mascagni, Pietro Nakawala, Hirenkumar Park, Adrian Pugh, Carla Stoyanov, Danail Vedula, Swaroop S. Cleary, Kevin Fichtinger, Gabor Forestier, Germain Gibaud, Bernard Grantcharov, Teodor Hashizume, Makoto Heckmann-Nötzel, Doreen Kenngott, Hannes G. Kikinis, Ron Mündermann, Lars Navab, Nassir Onogur, Sinan Sznitman, Raphael Taylor, Russell H. Tizabi, Minu D. Wagner, Martin Hager, Gregory D. Neumuth, Thomas Padoy, Nicolas Collins, Justin Gockel, Ines Goedeke, Jan Hashimoto, Daniel A. Joyeux, Luc Lam, Kyle Leff, Daniel R. Madani, Amin Marcus, Hani J. Meireles, Ozanan Seitel, Alexander Teber, Dogu Ückert, Frank Müller-Stich, Beat P. Jannin, Pierre Speidel, Stefanie Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey LTSI Inserm UMR 1099 University of Rennes 1 Rennes France IRCAD Strasbourg France The Malone Center for Engineering in Healthcare The Johns Hopkins University BaltimoreMD United States KARL STORZ SE and Co. KG Tuttlingen Germany Department of Surgery Klinikum Rechts der Isar Technical University of Munich Munich Germany The Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom ICube University of Strasbourg CNRS France IHU Strasbourg Strasbourg France Altair Robotics Lab University of Verona Verona Italy Department of Surgery Anne Arundel Health System AnnapolisMD United States Johns Hopkins University School of Medicine BaltimoreMD United States Department of Surgery Stanford University School of Medicine StanfordCA United States Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany The Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital WashingtonDC United States The Perk Lab Queen's University KingstonON Canada University of Haute-Alsace Mulhouse France Faculty of Information Technology Monash University ClaytonVIC Australia University of Toronto TorontoON Canada The Li Ka Shing Knowledge Institute of St. Michael's Hospital TorontoON Canada Kyushu University Fukuoka Japan Kitakyushu Koga Hospital Fukuoka Japan Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Computer Science The Johns Hopkins University Baltim

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process. © 2020, CC BY.

关键词： Deep learning

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Erratum for Wang et al., "Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin"

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Microbiology spectrum 2022年第1期10卷 e0261021页

作者： Hsin-Yao Wang Chia-Ru Chung Chao-Jung Chen Ko-Pei Lu Yi-Ju Tseng Tzu-Hao Chang Min-Hsien Wu Wan-Ting Huang Ting-Wei Lin Tsui-Ping Liu Tzong-Yi Lee Jorng-Tzong Horng Jang-Jih Lu Department of Laboratory Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Computer Science and Information Engineering National Central Universitygrid.37589.30 Taoyuan City Taiwan. Graduate Institute of Integrated Medicine China Medical Universitygrid.254145.3 Taichung Taiwan. Proteomics Core Laboratory China Medical Universitygrid.254145.3 Hospital Taichung Taiwan. Graduate Program in Biomedical Information Yuan-Ze University Taoyuan City Taiwan. Department of Information Management Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Division of Haematology/Oncology Department of Internal Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Biosensor Group Biomedical Engineering Research Center Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Pathology Kaohsiung Chang Gung Memorial Hospitalgrid.413801.f and Chang Gung University College of Medicine Kaohsiung Taiwan. School of Life and Health Sciences The Chinese University of Hong Kong Shenzhen China. Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen China. Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan. School of Medicine Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung Universitygrid.145695.a Taoyuan City Taiwan.

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

arXiv

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

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NPJ digital medicine 2022年第1期5卷 56页

作者： Qi Dou Tiffany Y So Meirui Jiang Quande Liu Varut Vardhanabhuti Georgios Kaissis Zeju Li Weixin Si Heather H C Lee Kevin Yu Zuxin Feng Li Dong Egon Burian Friederike Jungmann Rickmer Braren Marcus Makowski Bernhard Kainz Daniel Rueckert Ben Glocker Simon C H Yu Pheng Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR China. qdou@cse.cuhk.edu.hk. Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong SAR China. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR China. Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong SAR China. Biomedical Image Analysis Group Imperial College London London UK. Institute for Diagnostic and Interventional Radiology Technical University of Munich School of Medicine Munich Germany. OpenMined Oxford UK. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China. Department of Diagnostic Radiology Princess Margaret Hospital Hong Kong SAR China. Department of Radiology Tuen Muen Hospital Hong Kong SAR China. Department of Emergency Medicine Peking University ShenZhen Hospital Shenzhen Guangdong China. Department of Radiology Zhijiang People's Hospital Zhijiang Hubei China. German Cancer Research Center (DKFZ) Heidelberg Germany. AI in Medicine and Healthcare Technical University of Munich School of Informatics and Medicine Munich Germany. Biomedical Image Analysis Group Imperial College London London UK. b.glocker@imperial.ac.uk. Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong SAR China. simonyu@cuhk.edu.hk. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR China. pheng@cse.cuhk.edu.hk.

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The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

arXiv

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arXiv 2020年

作者： Marinescu, Razvan V. Oxtoby, Neil P. Young, Alexandra L. Bron, Esther E. Toga, Arthur W. Weiner, Michael W. Barkhof, Frederik Fox, Nick C. Eshaghi, Arman Ansart, Manon Durrleman, Stanley Lu, Pascal Iddi, Samuel Li, Dan Thompson, Wesley K. Donohue, Michael C. Nahon, Aviv Levy, Yarden Halbersberg, Dan Cohen, Mariya Liao, Huiling Li, Tengfei Yu, Kaixian Zhu, Hongtu Tamez-Peña, José G. Ismail, Aya Wood, Timothy Bravo, Hector Corrada Nguyen, Minh Sun, Nanbo Feng, Jiashi Thomas Yeo, B.T. Chen, Gang Qi, Ke Chen, Shiyang Qiu, Deqiang Buciuman, Ionut Kelner, Alex Pop, Raluca Rimocea, Denisa Ghazi, Mostafa M. Nielsen, Mads Ourselin, Sebastien Sørensen, Lauge Venkatraghavan, Vikram Liu, Keli Rabe, Christina Manser, Paul Hill, Steven M. Howlett, James Huang, Zhiyue Kiddle, Steven Mukherjee, Sach Rouanet, Anaïs Taschler, Bernd Tom, Brian D.M. White, Simon R. Faux, Noel Sedai, Suman de Velasco Oriol, Javier Clemente, Edgar E.V. Estrada, Karol Aksman, Leon Altmann, Andre Stonnington, Cynthia M. Wang, Yalin Wu, Jianfeng Devadas, Vivek Fourrier, Clementine Raket, Lars Lau Sotiras, Aristeidis Erus, Guray Doshi, Jimit Davatzikos, Christos Vogel, Jacob Doyle, Andrew Tam, Angela Diaz-Papkovich, Alex Jammeh, Emmanuel Koval, Igor Moore, Paul Lyons, Terry J. Gallacher, John Tohka, Jussi Ciszek, Robert Jedynak, Bruno Pandya, Kruti Bilgel, Murat Engels, William Cole, Joseph Golland, Polina Klein, Stefan Alexander, Daniel C. Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Netherlands Laboratory of NeuroImaging University of Southern California United States Center for Imaging of Neurodegenerative Diseases University of California San Francisco United States Department of Radiology and Nuclear Medicine VU Medical Centre Netherlands Dementia Research Centre The UK Dementia Research Institute UCL Queen Square Institute of Neurology United Kingdom Institut du Cerveau et de la Moelle épinière Paris France Alzheimer's Therapeutic Research Institute University of Southern California United States Department of Statistics and Actuarial Science University of Ghana Ghana Department of Family Medicine and Public Health University of California San Diego United States Ben Gurion University of the Negev Beersheba Israel The University of Texas Health Science Center at Houston Houston United States Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey Mexico University of Maryland College Park United States National University of Singapore Singapore Singapore Medical College of Wisconsin Milwaukee United States Emory University Atlanta United States Georgia Institute of Technology Atlanta United States Vasile Lucaciu National College Baia Mare Romania Biomediq A/S Denmark Cerebriu A/S Denmark University of Copenhagen Denmark School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom Genentech United States MRC Biostatistics Unit University of Cambridge United Kingdom IBM Research Australia Melbourne Australia Brandeis University Waltham United States Mayo Clinic ScottsdaleAZ United States H. Lundbeck A/S Denmark Clinical Memory Research Unit Department of Clinical Sciences Malmo Lund University Lund Sweden Center

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADASCog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 perce

关键词： Forecasting

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Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling

arXiv

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arXiv 2023年

作者： Ligneul, Clémence Najac, Chloé Döring, André Beaulieu, Christian Branzoli, Francesca Clarke, William T. Cudalbu, Cristina Genovese, Guglielmo Jbabdi, Saad Jelescu, Ileana Karampinos, Dimitrios Kreis, Roland Lundell, Henrik Marjańska, Malgorzata Möller, Harald E. Mosso, Jessie Mougel, Eloïse Posse, Stefan Ruschke, Stefan Simsek, Kadir Szczepankiewicz, Filip Tal, Assaf Tax, Chantal Oeltzschner, Georg Palombo, Marco Ronen, Itamar Valette, Julien Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford United Kingdom C.J. Gorter MRI Center Department of Radiology Leiden University Medical Center Netherlands School of Psychology Cardiff University Cardiff United Kingdom Departments of Biomedical Engineering and Radiology University of Alberta EdmontonAB Canada Paris Brain Institute - ICM Sorbonne University UMR S 1127 Inserm U 1127 CNRS UMR 7225 ParisF-75013 France CIBM Center for Biomedical Imaging Switzerland Animal Imaging and Technology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Center for Magnetic Resonance Research Department of Radiology University of Minnesota MinneapolisMN United States Department of Radiology Lausanne University Hospital Lausanne Switzerland Faculty of Biology and Medicine University of Lausanne Lausanne Switzerland Department of Diagnostic and Interventional Radiology Technical University of Munich Munich Germany MR Methodology Department for Diagnostic and Interventional Neuroradiology University of Bern Switzerland Swiss Institute for Translational and Entrepreneurial Medicine Bern Switzerland Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager anf Hvidovre Denmark Department for Health Technology Technical University of Denmark Lyngby Denmark NMR Methods & Development Group Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany LIFMET EPFL Lausanne Switzerland Université Paris-Saclay CEA CNRS MIRCen Laboratoires des Maladies Neurodégénératives Fontenay-aux-Roses France Department of Neurology University of New Mexico School of Medicine AlbuquerqueNM87131 United States Department of Physics and Astronomy University of New Mexico School of Medicine AlbuquerqueNM87131 United States School of Computer Science and Informatics Cardiff University Cardiff United Kin

Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to non-invasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MR spectroscopy (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modelling and interpretation of results. These challenges were the motivation behind the organisation of the Lorentz Workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting and modelling dMRS data and provides links to useful resources. © 2023, CC BY-NC-ND.

关键词： Diffusion

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Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

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Exposome 2024年第1期4卷 osae001页

作者： Ming Kei Chung John S House Farida S Akhtari Konstantinos C Makris Michael A Langston Khandaker Talat Islam Philip Holmes Marc Chadeau-Hyam Alex I Smirnov Xiuxia Du Anne E Thessen Yuxia Cui Kai Zhang Arjun K Manrai Alison Motsinger-Reif Chirag J Patel Department of Biomedical Informatics Harvard Medical School Boston MA USA. School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong China. Institute of Environment Energy and Sustainability The Chinese University of Hong Kong Hong Kong China. Biostatistics and Computational Biology Branch Division of Intramural Research National Institute of Environmental Health Sciences Durham NC USA. Cyprus International Institute for Environmental and Public Health School of Health Sciences Cyprus University of Technology Limassol Cyprus. Department of Electrical Engineering and Computer Science University of TN Knoxville TN USA. Department of Population and Public Health Sciences Keck School of Medicine of the University of Southern CA Los Angeles CA USA. Department of Physics Villanova University Villanova Philadelphia USA. Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Chemistry NC State University Raleigh NC USA. Department of Bioinformatics and Genomics College of Computing and Informatics University of NC at Charlotte Charlotte NC USA. Department of Biomedical Informatics University of CO Anschutz Medical Campus Aurora CO USA. Exposure Response and Technology Branch Division of Extramural Research and Training National Institute of Environmental Health Sciences Durham NC USA. Department of Environmental Health Sciences School of Public Health University at Albany State University of NY Rensselaer NY USA.

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

关键词： Exposome-Wide Association Study (ExWAS) data science epidemiology exposome false discovery rate phenotype

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Publisher Correction: An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease

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Nature communications 2022年第1期13卷 4729页

作者： Pradipta Ghosh Gajanan D Katkar Chisato Shimizu Jihoon Kim Soni Khandelwal Adriana H Tremoulet John T Kanegaye Joseph Bocchini Soumita Das Jane C Burns Debashis Sahoo Department of Cellular and Molecular Medicine University of California San Diego San Diego USA. prghosh@ucsd.edu. Department of Medicine University of California San Diego San Diego USA. prghosh@ucsd.edu. Department of Cellular and Molecular Medicine University of California San Diego San Diego USA. Department of Pediatrics University of California San Diego San Diego USA. Rady Children's Hospital-San Diego San Diego CA USA. Department of Biomedical informatics University of California San Diego San Diego USA. Department of Computer Science and Engineering Jacob's School of Engineering University of California San Diego San Diego USA. Willis-Knighton Health System Shreveport LA USA. Department of Pathology University of California San Diego USA. Department of Pediatrics University of California San Diego San Diego USA. jcburns@health.ucsd.edu. Rady Children's Hospital-San Diego San Diego CA USA. jcburns@health.ucsd.edu. Department of Pediatrics University of California San Diego San Diego USA. dsahoo@ucsd.edu. Department of Computer Science and Engineering Jacob's School of Engineering University of California San Diego San Diego USA. dsahoo@ucsd.edu.

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Author Correction: The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing

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Nature neuroscience 2023年第10期26卷 1833页

作者： Rachel E Rodin Yanmei Dou Minseok Kwon Maxwell A Sherman Alissa M D'Gama Ryan N Doan Lariza M Rento Kelly M Girskis Craig L Bohrson Sonia N Kim Ajay Nadig Lovelace J Luquette Doga C Gulhan Peter J Park Christopher A Walsh Division of Genetics and Genomics Manton Center for Orphan Disease Research and Howard Hughes Medical Institute Boston Children's Hospital Boston MA USA. Departments of Pediatrics and Neurology Harvard Medical School Boston MA USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Department of Electrical Engineering and Computer Science MIT Cambridge MA USA. Division of Genetics Brigham and Women's Hospital Boston MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. peter_park@hms.harvard.edu. Division of Genetics Brigham and Women's Hospital Boston MA USA. peter_park@hms.harvard.edu. Division of Genetics and Genomics Manton Center for Orphan Disease Research and Howard Hughes Medical Institute Boston Children's Hospital Boston MA USA. Christopher.walsh@childrens.harvard.edu. Departments of Pediatrics and Neurology Harvard Medical School Boston MA USA. Christopher.walsh@childrens.harvard.edu. Broad Institute of MIT and Harvard Cambridge MA USA. Christopher.walsh@childrens.harvard.edu.

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