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arXiv 2018年

作者： Ozfatura, Emre Gündüz, Deniz Ulukus, Sennur Information Processing and Communications Lab Dept. of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD United States

When gradient descent (GD) is scaled to many parallel computing servers (CSs) for large scale machine learning problems, its per-iteration computation time is limited by the straggling servers. Coded distributed GD (DGD) can tolerate straggling servers by assigning redundant computations to the CSs, but in most existing schemes, each non-straggling CS transmits one message per iteration to the aggregating server (AS) after completing all its computations. We allow multiple computations to be conveyed from each CS per iteration in order to exploit computations executed also by the straggling servers. We show that the average completion time per iteration can be reduced significantly at a reasonable increase in the communication load. We also propose a general coded DGD technique which can trade-off the average computation time with the communication load. Copyright © 2018, The Authors. All rights reserved.

关键词： Gradient methods

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Queueing stability and CSI probing of a TDD wireless network with interference alignment

Queueing stability and CSI probing of a TDD wireless network...

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作者： Deghel, Matha Assaad, Mohamad Debbah, Mérouane Ephremides, Anthony TCL Chair on 5G Lab-oratoire de Signaux et Systèmes CNRS UMR8506 CentraleSupélec Gif-sur-Yvette91192 France Large Systems and Networks Group CentraleSupélec Gif-sur-Yvette France Mathematical and Algorithmic Sciences Laboratory Huawei Technologies France Boulogne–Billancourt92100 France Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland at College Park College ParkMD20742 United States

This paper characterizes the performance in terms of queueing stability of a network composed of multiple MIMO transmitter-receiver pairs taking into account the dynamic traffic pattern and the probing/feedback cost. We adopt a centralized scheduling scheme that selects a number of active pairs in each time-slot. We consider that the transmitters apply interference alignment (IA) technique if two or more pairs are active, whereas in the special case where one pair is active point-to-point MIMO singular value decomposition (SVD) is used. We consider a time-division duplex (TDD) system where transmitters acquire their channel state information (CSI) by decoding the pilot sequences sent by the receivers. Since global CSI knowledge is required for IA, the transmitters have also to exchange their estimated CSIs over a backhaul of limited capacity (i.e. imperfect case). Under this setting, we characterize in this paper the stability region of the system under both the imperfect and perfect (i.e. unlimited backhaul) cases, then we examine the gap between these two resulting regions. Further, under each case we provide a centralized probing policy that achieves the max stability region. These stability regions and scheduling policies are given for the symmetric system, where all the path loss coefficients are equal to each other, as well as for the general system. For the symmetric system, we provide the conditions under which IA yields a queueing stability gain compared to SVD. Under the general system, the adopted scheduling policy is of a high computational complexity for moderate numbers of pairs, consequently we propose an approximate policy that has a reduced complexity but that achieves only a fraction of the system stability region. A characterization of this fraction is provided. © 2017 IEEE.

关键词： Channel state information

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Reports of the workshops of the 32nd AAAI conference on artificial intelligence

Reports of the workshops of the 32nd AAAI conference on arti...

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作者： Bouchard, Bruno Bouchard, Kevin Brown, Noam Chhaya, Niyati Farchi, Eitan Gaboury, Sebastien Geib, Christopher Gyrard, Amelie Jaidka, Kokil Keren, Sarah Khardon, Roni Kordjamshidi, Parisa Martinez, David Mattei, Nicholas Michalowski, Martin Mirsky, Reuth Osborn, Joseph Sahin, Cem Shehory, Onn Shaban-Nejad, Arash Sheth, Amit Shimshoni, Ilan Shrobe, Howie Sinha, Arunesh Sinha, Atanu R. Srivastava, Biplav Streilein, William Theocharous, Georgios Venable, K. Brent Wagner, Neal Zamansky, Anna Université du Québec ChicoutimiQC Canada Computer Science Carnegie Mellon University United States Big Data Experience Lab Adobe Research Bangalore India IBM Research Haifa Research Lab. Israel Smart Information Flow Technologies United States Kno.e.sis Wright State University United States University of Pennsylvania United States Harvard University United States Computer Science Tufts University United States Tulane University IHMC United States MIT Lincoln Laboratory United States IBM Research TJ Watson Research Center United States University of Minnesota School of Nursing United States ISE Department Ben Gurion University United States Computer Science Department Pomona College United States Bar Ilan University Israel University of Tennessee Health Science Center United States Information Systems Department University of Haifa Israel Massachusetts Institute of Technology United States University of Southern California United States AI Department IBM Research Yorktown HeightsNY United States Adobe Research San JoseCA United States

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The science case and challenges of spaceborne sub-millimeter interferometry: the study case of TeraHertz Exploration and Zooming-in for Astrophysics (THEZA)

The science case and challenges of spaceborne sub-millimeter...

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IAF Symposium on Ongoing and Near Future Space Astronomy and Solar-System Science Missions 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Gurvits, Leonid I. Paragi, Zsolt Amils, Ricardo I. van Bemmel, Ilse Boven, Paul Casasola, Viviana Conway, John Davelaar, Jordy Díez-González, M. Carmen Falcke, Heino Fender, Rob Frey, Sándor Fromm, Christian M. Gallego-Puyol, Juan D. García-Miró, Cristina Garrett, Michael A. Giroletti, Marcello Goddi, Ciriaco Gómez, José L. van der Gucht, Jeffrey Guirado, José Carlos Haiman, Zoltán Helmich, Frank Hudson, Ben Humphreys, Elizabeth Impellizzeri, Violette Janssen, Michael Johnson, Michael D. Kovalev, Yuri Y. Kramer, Michael Lindqvist, Michael Linz, Hendrik Liuzzo, Elisabetta Lobanov, Andrei P. López-Fernández, Isaac Malo-Gómez, Inmaculada Masania, Kunal Mizuno, Yosuke Plavin, Alexander V. Rajan, Raj T. Rezzolla, Luciano Roelofs, Freek Ros, Eduardo Rygl, Kazi L.J. Savolainen, Tuomas Schuster, Karl Venturi, Tiziana Verkouter, Harro de Vicente, Pablo Visser, Pieter N.A.M. Wiedner, Martina C. Wielgus, Maciek Wiik, Kaj Zensus, J. Anton Joint Institute for VLBI ERIC Dwingeloo Netherlands Department of Astrodynamics and Space Missions Delft University of Technology Delft Netherlands Yebes Observatory IGN Spain INAF Institute of Radio Astronomy Bologna Italy Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory Onsala Sweden Department of Astronomy and Astrophysics Laboratory Columbia University New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Radboud University Nijmegen Netherlands Oxford University United Kingdom Konkoly Observatory Res. Centre for Astronomy and Earth Sciences Budapest Hungary Institute of Physics ELTE Eötvös Loránd University Budapest Hungary Goethe-Universität Frankfurt Germany Jodrell Bank Centre for Astrophysics The University of Manchester United Kingdom Leiden Observatory Leiden University Netherlands Instituto de Astrofísica de Andalucía – CSIC Granada Spain Observatorio Astronómico University of Valencia Spain SRON Netherlands Institute for Space Research Netherlands Kapteyn Institute University of Groningen Netherlands KISPE Space Systems Limited United Kingdom ESO Garching Germany Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Black Hole Initiative Harvard University CambridgeMA02138 United States Astro Space Center Lebedev Physical Institute Moscow Russia Moscow Institute of Physics and Technology Dolgoprudnyi Russia Max Planck Institute for Astronomy Heidelberg Germany Shaping Matter Lab Faculty of Aerospace Engineering TU Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Aalto University Department of Electronics and Nanoengineering Finland Aalto University Metsähovi Radio Observatory Finland IRAM Grenoble France Observ

ISBN: (纸本)9781713843009

Ultra-high angular resolution in astronomy has always been an important vehicle for making fundamental discoveries. Recent results in direct imaging of the vicinity of the super-massive black hole in the nucleus of the radio galaxy M87 by the millimeter VLBI system Event Horizon Telescope (EHT) and various pioneering results of the Space VLBI mission RadioAstron provided new momentum in high angular resolution astrophysics. In both mentioned cases, the angular resolution reached the values of about 10−20 microrcseconds (0.05−0.1 nanoradian). Angular resolution is proportional to the observing wavelength and inversely proportional to the interferometer baseline length. In the case of Earth-based EHT, the highest angular resolution was achieved by combining the shortest possible wavelength of 1.3 mm with the longest possible baselines, comparable to the Earth’s diameter. For RadioAstron, operational wavelengths were in the range from 92 cm down to 1.3 cm, but the baselines were as long as ∼350,000 km. However, these two highlights of radio astronomy, EHT and RadioAstron do not"saturate" the interest to further increase in angular resolution. Quite opposite: the science case for further increase in angular resolution of astrophysical studies becomes even stronger. A natural and, in fact, the only possible way of moving forward is to enhance mm/sub-mm VLBI by extending baselines to extraterrestrial dimensions, i.e. creating a mm/sub-mm Space VLBI system. The inevitable move toward space-borne mm/sub-mm VLBI is a subject of several concept studies. In this presentation we will focus on one of them called TeraHertz Exploration and Zooming-in for Astrophysics (THEZA), prepared in response to the ESA’s call for its next major science program Voyage 2050 (Gurvits et al. 2021). The THEZA rationale is focused at the physics of spacetime in the vicinity of super-massive black holes as the leading science drive. However, it will also open up a sizable new range of hitherto unrea

关键词： Astrophysics

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Common Limitations of Image Processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg 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 Division of Medical Image Computing Heidelberg 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 Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

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Cell 2025年第12期188卷 3167-3183.e18页

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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Agent-Based Modelling and Simulation for evacuation of people from a building in case of fire

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Procedia computer Science 2018年 130卷 10-17页

作者： Selain Kasereka Nathanaël Kasoro Kyandoghere Kyamakya Emile-Franc Doungmo Goufo Abiola P. Chokki Maurice V. Yengo University of Kinshasa Mathematics and Computer Science Department Artificial and Business Intelligence Lab Kinshasa DR. Congo University of South Africa College of Science Engineering & Technology Department of Mathematical Sciences Florida South Africa Vietnam National University Institut Francophone International Hanoi Vietnam Alpen-Adria-Universitaet Klagenfurt Institute of Smart Systems Technologies Klagenfurt Austria

The evacuation of people from a building on fire is a task which can prove to be very difficult, in particular because of the influence of human behavior, but also of the type of people or the evacuation place configuration. Thus, it is crucial to think on how to organize the evacuation upstream for a situation of emergency can give rise disorganization, on one hand because of panic which grips evacuees, and on the other end because of the large quantity of evacuees in dangerous conditions. These recent years, several fire evacuation models have been proposed. Unfortunately, most of these models do not clearly define the parameters to be considered for their effective evaluations. These models consider, more generally, the number of survivors as a key parameter of evaluation. The purpose of this paper is to propose an intelligent Agent-Based Model enabling the modelling and simulation of evacuation of people from a building on fire. Our proposed model is based on four parameters that allow her practical evaluation. A case study of simulation is carried out in a building having the general configuration of Kinshasa supermarkets. This model is general enough for it to be implemented in several types of commercial buildings without major changes.

关键词： Modelling Simulation Fire Evacuation Intelligent Agents Evaluation parameters

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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