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SSRN 2024年

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

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

关键词： Medical computing

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The overlapping burden of the three leading causes of disability and death in sub-Saharan African children

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Nature Communications 2022年第1期13卷 1-14页

作者： Reiner, Robert C. Welgan, Catherine A. Troeger, Christopher E. Baumann, Mathew M. Weiss, Daniel J. Deshpande, Aniruddha Blacker, Brigette F. Miller-Petrie, Molly K. Earl, Lucas Bhatt, Samir Abolhassani, Hassan Abosetugn, Akine Eshete Abu-Gharbieh, Eman Adekanmbi, Victor Adetokunboh, Olatunji O. Aghaali, Mohammad Aji, Budi Alahdab, Fares Al-Aly, Ziyad Alhassan, Robert Kaba Ali, Saqib Alizade, Hesam Aljunid, Syed Mohamed Almasi-Hashiani, Amir Al-Mekhlafi, Hesham M. Altirkawi, Khalid A. Alvis-Guzman, Nelson Amare, Azmeraw T. Amini, Saeed Amugsi, Dickson A. Ancuceanu, Robert Andrei, Catalina Liliana Ansari, Fereshteh Anvari, Davood Appiah, Seth Christopher Yaw Arabloo, Jalal Aremu, Olatunde Atout, Maha Moh’d Wahbi Ausloos, Marcel Ausloos, Floriane Ayanore, Martin Amogre Aynalem, Yared Asmare Azene, Zelalem Nigussie Badawi, Alaa Baig, Atif Amin Banach, Maciej Bedi, Neeraj Bhagavathula, Akshaya Srikanth Bhandari, Dinesh Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bhutta, Zulfiqar A. Bijani, Ali Birhanu, Tesega Tesega Mengistu Bitew, Zebenay Workneh Boloor, Archith Brady, Oliver J. Butt, Zahid A. Car, Josip Carvalho, Felix Casey, Daniel C. Chattu, Vijay Kumar Chowdhury, Mohiuddin Ahsanul Kabir Chu, Dinh-Toi Coelho, Camila H. Cook, Aubrey J. Damiani, Giovanni Daoud, Farah Gela, Jiregna Darega Darwish, Amira Hamed Daryani, Ahmad Das, Jai K. Davis Weaver, Nicole Deribe, Kebede Desalew, Assefa Dharmaratne, Samath Dhamminda Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Dorostkar, Fariba Dubljanin, Eleonora Duko, Bereket Dwyer-Lindgren, Laura Effiong, Andem El Sayed Zaki, Maysaa El Tantawi, Maha Enany, Shymaa Fattahi, Nazir Feigin, Valery L. Fernandes, Eduarda Ferrara, Pietro Fischer, Florian Foigt, Nataliya A. Folayan, Morenike Oluwatoyin Foroutan, Masoud Frostad, Joseph Jon Fukumoto, Takeshi Gaidhane, Abhay Motiramji Gebrekrstos, Hailemikael Gebrekidan G. K. Gebremeskel, Leake Gebreslassie, Assefa Ayalew Gething, Peter W. Gezae, Kebede Embaye Ghadiri, Keyghobad Ghashghaee, Ahmad Golechha, Mahaveer Gubar Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Malaria Atlas Project University of Oxford Oxford United Kingdom Imperial College London London United Kingdom Department of Laboratory Medicine Karolinska University Hospital Huddinge Sweden Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Public Health Debre Berhan University Debre Berhan Ethiopia Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Population Health Sciences King’s College London London United Kingdom Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Stellenbosch South Africa Department of Global Health Stellenbosch University Cape Town South Africa Department of Epidemiology and Biostatistics Qom University of Medical Sciences Qom Iran Faculty of Medicine and Public Health Jenderal Soedirman University Purwokerto Indonesia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center Department of Veterans Affairs St Louis MO United States Institute of Health Research University of Health and Allied Sciences Ho Ghana Department of Information Systems College of Economics and Political Science Sultan Qaboos University Muscat Oman Infectious and Tropical Disease Research Center Hormozgan University of Medical Sciences Bandar Abbas Iran Department of Health Policy and Management Kuwait University Safat Kuwait International Centre for Casemix and Clinical Coding National University of Malaysia Bandar Tun Razak Malaysia Department of Epidemiology Arak University of Medical Sciences Arak Iran Medical Research Center

Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival. © 2022, The Author(s).

关键词： Biogeography Epidemiology Infectious diseases Malaria

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Building an international consortium for tracking coronavirus health status (vol 78, pg 931, 2020)

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NATURE MEDICINE 2020年第8期26卷 1309-1309页

作者： Segal, Eran Zhang, Feng Lin, Xihong King, Gary Shalem, Ophir Shilo, Smadar Allen, William E. Alquaddoomi, Faisal Altae-Tran, Han Anders, Simon Balicer, Ran Bauman, Tal Bonilla, Ximena Booman, Gisel Chan, Andrew T. Cohen, Ori Coletti, Silvano Davidson, Natalie Dor, Yuval Drew, David A. Elemento, Olivier Evans, Georgina Ewels, Phil Gale, Joshua Gavrieli, Amir Geiger, Benjamin Grad, Yonatan H. Greene, Casey S. Hajirasouliha, Iman Jerala, Roman Kahles, Andre Kallioniemi, Olli Keshet, Ayya Kocarev, Ljupco Landua, Gregory Meir, Tomer Muller, Aline Nguyen, Long H. Oresic, Matej Ovchinnikova, Svetlana Peterson, Hedi Prodanova, Jana Rajagopal, Jay Raetsch, Gunnar Rossman, Hagai Rung, Johan Sboner, Andrea Sigaras, Alexandros Spector, Tim Steinherz, Ron Stevens, Irene Vilo, Jaak Wilmes, Paul Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Howard Hughes Medical Institute Chevy Chase Maryland USA Broad Institute of MIT and Harvard Cambridge MA USA Departments of Biostatistics and Statistics Harvard T.H. Chan School of Public Health Harvard University Cambridge MA USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Pediatric Diabetes Unit Ruth Rappaport Children’s Hospital Rambam Healthcare Campus Haifa Israel Harvard Society of Fellows Harvard University Cambridge MA USA ETH Zurich NEXUS Personalized Health Technologies Zurich Switzerland Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Center for Molecular Biology (ZMBH) University of Heidelberg Heidelberg Germany Clalit Research Institute Clalit Health Services Ramat Gan Israel Mapping and Geo-Information Engineering Civil and Environmental Engineering Faculty The Technion Haifa Israel ETH Zurich Department for Computer Science Zurich University Hospital Zurich Medical Informatics Zurich and SIB Swiss Institute of Bioinformatics Zurich Switzerland Regen Network Mar del Plata Argentina Massachusetts General Hospital (MGH) Boston MA USA Chelonia Applied Science Allschwil Switzerland IMRIC Developmental Biology and Cancer Research School of Medicine The Hebrew University Jerusalem Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Science for Life Laboratory (SciLifeLab) Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden http://*** Canada Department of Immunology Weizmann Institute of Scienc

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

关键词： computational platforms and environments Databases Infectious diseases

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Data Science and symbolic AI: Synergies, challenges and opportunities

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Data Science 2017年第1-2期1卷 27-38页

作者： Hoehndorf, Robert Queralt-Rosinach, Núria Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Integrative Structural and Computational Biology The Scripps Research Institute La Jolla United States

Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable;they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information;and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists' toolbox. © 2017 - IOS Press and the authors.

关键词： empirical science machine learning statistics Symbolic AI

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Robust field-level likelihood-free inference with galaxies

arXiv

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arXiv 2023年

作者： de Santi, Natalí S.M. Shao, Helen Villaescusa-Navarro, Francisco Abramo, L. Raul Teyssier, Romain Villanueva-Domingo, Pablo Ni, Yueying Anglés-Alcázar, Daniel Genel, Shy Hernandez-Martinez, Elena Steinwandel, Ulrich P. Lovell, Christopher C. Dolag, Klaus Castro, Tiago Vogelsberger, Mark Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Instituto de Física Universidade de São Paulo R. do Matão 1371 São Paulo05508-900 Brazil Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Bellaterra Barcelona08193 Spain Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269 United States Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr. 1 München81679 Germany Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom Centre for Astrophysics Research School of Physics Engineering & Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34143 Italy INFN Sezione di Trieste Via Valerio 2 TS TriesteI-34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Kavli Institute for Astrophysics and Space Research Department of Physics MIT CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA02139 United States

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain 3D positions and radial velocities of ∼ 1, 000 galaxies in tiny (25 h−1Mpc)3 volumes our models can infer the value of Ωm with approximately 12% precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and AGN feedback, run with five different codes and subgrid models – IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE –, we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on 1,024 simulations that cover a vast region in parameter space – variations in 5 cosmological and 23 astrophysical parameters – finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than ∼ 10 h−1kpc. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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Quantum Simulation for High Energy Physics

arXiv

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arXiv 2022年

作者： Bauer, Christian W. Davoudi, Zohreh Balantekin, A. Baha Bhattacharya, Tanmoy Carena, Marcela de Jong, Wibe A. Draper, Patrick El-Khadra, Aida Gemelke, Nate Hanada, Masanori Kharzeev, Dmitri Lamm, Henry Li, Ying-Ying Liu, Junyu Lukin, Mikhail Meurice, Yannick Monroe, Christopher Nachman, Benjamin Pagano, Guido Preskill, John Rinaldi, Enrico Roggero, Alessandro Santiago, David I. Savage, Martin J. Siddiqi, Irfan Siopsis, George Van Zanten, David Wiebe, Nathan Yamauchi, Yukari Yeter-Aydeniz, Kübra Zorzetti, Silvia Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Physics University of Wisconsin MadisonWI53706 United States T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Enrico Fermi Institute University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois UrbanaIL61801 United States QuEra Computing Inc BostonMA02135 United States Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University NY11794-3800 United States Department of Physics Brookhaven National Laboratory UptonNY11973 United States Pritzker School of Molecular Engineering Chicago Quantum Exchange Kadanoff Center for Theoretical Physics University of Chicago IL60637 United States QBraid Co. Harper Court 5235 ChicagoIL60615 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Duke Quantum Center Duke University DurhamNC27701 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Physics and Astronomy Department Rice University HoustonTX77005 United States Institute for Quantum Information and Matter Californi

It is for the first time that quantum simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in Quantum Information Sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This community whitepaper is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond. © 2022, CC BY.

关键词： High energy physics

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Publisher Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2023年第1期55卷 166页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Paul Jongjoon Choi Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

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A universal equation to predict Ωmfrom halo and galaxy catalogues

arXiv

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arXiv 2023年

作者： Shao, Helen De Santi, Natalí S.M. Villaescusa-Navarro, Francisco Teyssier, Romain Ni, Yueying Anglés-Alcázar, Daniel Genel, Shy Steinwandel, Ulrich P. Hernández-Martínez, Elena Dolag, Klaus Lovell, Christopher C. Garrison, Lehman H. Visbal, Eli Kulkarni, Mihir Hernquist, Lars Castro, Tiago Vogelsberger, Mark Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Instituto de Física Universidade de São Paulo R. do Matão 1371 São Paulo05508-900 Brazil Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269 United States Columbia Astrophysics Laboratory Columbia University New YorkNY10027 United States Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr. 1 München81679 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom Centre for Astrophysics Research School of Physics Engineering & Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Department of Physics and Astronomy Ritter Astrophysical Research Center University of Toledo 2801 W Bancroft Street ToledoOH43606 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34143 Italy INFN Sezione di Trieste Via Valerio 2 TS TriesteI-34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Kavli Institute for Astrophysics and Space Research Department of Physics Mit CambridgeMA02139 United States The Nsf Ai Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA02139 United States

We discover analytic equations that can infer the value of Ωmfrom the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer Ωmwith ∼ 6% accuracy from halo catalogues of thousands of N-body simulations run with six different codes: Abacus, CUBEP3M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict Ωmfrom halo catalogues of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that by tuning a single free parameter, our equations can also infer the value of Ωmfrom galaxy catalogues of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogues from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and Ωm, one that is not affected by galaxy formation physics down to scales as small as 10 h-1kpc. © 2023, CC BY.

关键词： Galaxies

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Unlocking capacities of viral genomics for the COVID-19 pandemic response

arXiv

引用

arXiv 2021年

作者： Knyazev, Sergey Chhugani, Karishma Sarwal, Varuni Ayyala, Ram Singh, Harman Karthikeyan, Smruthi Deshpande, Dhrithi Comarova, Zoia Lu, Angela Porozov, Yuri Wu, Aiping Abedalthagafi, Malak S. Nagaraj, Shivashankar H. Smith, Adam L. Skums, Pavel Ladner, Jason Lam, Tommy Tsan-Yuk Wu, Nicholas C. Zelikovsky, Alex Knight, Rob Crandall, Keith A. Mangul, Serghei Department of Computer Science College of Art and Science Georgia State University 1 Park Place AtlantaGA30303 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089 United States Department of Computer Science University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Neuroscience College of Life Sciences University of California Los Angeles 580 Portola Plaza Los AngelesCA90095 United States Department of Electrical Engineering Indian Institute of Technology Hauz Khas New Delhi110016 India Department of Pediatrics University of California San Diego San DiegoCA United States Paradigm Environmental 3911 Old Lee Highway FairfaxVA22030 United States Department of Pharmacology and Pharmaceutical Sciences School of Pharmacy University of Southern California 1985 Zonal Avenue Los AngelesCA90089-9121 United States World-Class Research Center Digital Biodesign and Personalized Healthcare I.M. Sechenov First Moscow State Medical University Moscow Russia Department of Computational Biology Sirius University of Science and Technology Sochi Russia Center for Systems Medicine Institute of Basic Medical Sciences Chinese Academy of Medical Sciences Peking Union Medical College Beijing100005 China Suzhou Institute of Systems Medicine Suzhou215123 China Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Centre for Genomics and Personalised Health Queensland University of Technology BrisbaneQLD4059 Australia Translational Research Institute Brisbane Australia Astani Department of Civil and Environmental Engineering University of Southern California 3620 South Vermont Avenue Los AngelesCA90089 United States Department of Computer Science College of Art and Science Georgia State University 1 Park Place Atlanta

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19. © 2021, CC BY-NC-ND.

关键词： SARS

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