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arXiv 2024年

作者： Radtke, Andrea J. Anidi, Ifeanyichukwu Arakkal, Leanne Arroyo-Mejias, Armando Beuschel, Rebecca T. Börner, Katy Chu, Colin J. Clark, Beatrice Clatworthy, Menna R. Colautti, Jake Croteau, Joshua Denha, Saven Dever, Rose Dutra, Walderez O. Fritzsche, Sonja Fullam, Spencer Gerner, Michael Y. Gola, Anita Gollob, Kenneth J. Hernandez, Jonathan M. Hor, Jyh Liang Ichise, Hiroshi Jing, Zhixin Jonigk, Danny Kandov, Evelyn Kastenmüller, Wolfgang Koenig, Joshua F.E. Kothurkar, Aanandita Kreins, Alexandra Y. Lamborn, Ian Lin, Yuri Morais, Katia Luciano Pereira Lunich, Aleksandra Luz, Jean C.S. MacDonald, Ryan B. Makranz, Chen Maltez, Vivien I. Moriaty, Ryan V. Ocampo-Godinez, Juan M. Olyntho, Vitoria M. Padhan, Kartika Remmert, Kirsten Richoz, Nathan Schrom, Edward C. Shang, Wanjing Shi, Lihong Shih, Rochelle M. Speranza, Emily Stierli, Salome Teichmann, Sarah A. Veres, Tibor Z. Vierhout, Megan Wachter, Brianna T. Wade-Vallance, Adam K. Williams, Margaret Zangger, Nathan Germain, Ronald N. Yaniv, Ziv Lymphocyte Biology Section Center for Advanced Tissue Imaging Laboratory of Immune System Biology National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Critical Care Medicine and Pulmonary Branch National Heart Lung and Blood Institute National Institutes of Health BethesdaMD United States Lymphocyte Biology Section Laboratory of Immune System Biology National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States UCL Institute of Ophthalmology NIHR Moorfields Biomedical Research Centre London United Kingdom Cambridge Institute for Therapeutic Immunology and Infectious Diseases University of Cambridge Department of Medicine Molecular Immunity Unit Laboratory of Molecular Biology Cambridge United Kingdom McMaster Immunology Research Centre Schroeder Allergy and Immunology Research Institute Department of Medicine Faculty of Health Sciences McMaster University HamiltonON Canada Department of Business Development BioLegend Inc. San DiegoCA United States Functional Immunogenomics Unit National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health BethesdaMD United States Laboratory of Cell-Cell Interactions Department of Morphology Institute of Biological Sciences Universidade Federal de Minas Gerais MG Belo Horizonte Brazil Spatial Proteomics Group Berlin Germany Division of Rheumatology Rush University Medical Center ChicagoIL United States Department of Immunology University of Washington School of Medicine SeattleWA United States Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development The Rockefeller University New YorkNY United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Surgical Oncology Program National Cancer Institute National Institutes of Health BethesdaMD United St

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, p... 详细信息

关键词： Cleaning

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Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

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NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

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

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Machine learning for early prediction of circulatory failure in the intensive care unit

arXiv

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arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Causes of evolutionary divergence in prostate cancer

arXiv

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arXiv 2025年

作者： Esentürk, Emre Sahli, Atef Haberland, Valeriia Ziuboniewicz, Aleksandra Wirth, Christopher Bova, G. Steven Bristow, Robert G. Brook, Mark N. Brors, Benedikt Butler, Adam Cancel-Tassin, Géraldine Cheng, Kevin C.L. Cooper, Colin S. Corcoran, Niall M. Cussenot, Olivier Eeles, Ros A. Favero, Francesco Gerhauser, Clarissa Gihawi, Abraham Girma, Etsehiwot G. Gnanapragasam, Vincent J. Gruber, Andreas J. Hamid, Anis Hayes, Vanessa M. He, Housheng Hansen Hovens, Christopher M. Imada, Eddie Luidy Jakobsdottir, G. Maria Jung, Chol-Hee Khani, Francesca Kote-Jarai, Zsofia Lamy, Philippe Leeman, Gregory Loda, Massimo Lutsik, Pavlo Marchionni, Luigi Molania, Ramyar Papenfuss, Anthony T. Pellegrina, Diogo Pope, Bernard Queiroz, Lucio R. Rausch, Tobias Reimand, Jüri Robinson, Brian Schlomm, Thorsten Sørensen, Karina D. Uhrig, Sebastian Weischenfeldt, Joachim Xu, Yaobo Yamaguchi, Takafumi N. Zanettini, Claudio Lynch, Andy G. Wedge, David C. Brewer, Daniel S. Woodcock, Dan J. Nuffield Department of Medicine University of Oxford United Kingdom Manchester Cancer Research Centre The University of Manchester United Kingdom Norwich Medical School University of East Anglia United Kingdom Nuffield Department of Surgical Sciences University of Oxford United Kingdom Prostate Cancer Research Center Faculty of Medicine and Health Technology Tampere University Finland Manchester Cancer Research Centre Cancer Research UK Manchester Institute The University of Manchester United Kingdom Christie NHS Foundation Trust United Kingdom Div Cancer Sciences Faculty of Biology Medicine & Health University of Manchester United Kingdom The Institute of Cancer Research United Kingdom Germany Germany Medical Faculty Heidelberg Faculty of Biosciences Heidelberg University Germany Wellcome Sanger Institute United Kingdom France Sorbonne Université GRC n°5 Predictive Onco-Urology APHP Tenon Hospital France Computational Biology Program Ontario Institute for Cancer Research Canada Department of Medical Biophysics University of Toronto Canada Department of Urology Royal Melbourne Hospital Australia Department of Surgery The University of Melbourne Australia Department of Urology Western Health Australia The Royal Marsden NHS Foundation Trust London United Kingdom University of Copenhagen Denmark Finsen Laboratory Copenhagen University Hospital - Rigshospitalet Denmark Germany Cambridge Biomedical Campus Addenbrooke's Hospital Site S Wards Building United Kingdom University of Konstanz Germany School of Medical Sciences University of Sydney Faculty of Medicine & Health Australia School of Health Systems & Public Health University of Pretoria South Africa Manchester Cancer Research Centre University of Manchester United Kingdom Princess Margaret Cancer Centre University Health Network Department of Medical Biophysics University of Toronto Canada Department of Surgery The Collaborative Centre for Genomic Cancer Medicine The University of Melbourne

Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional methods are unsuitable due to the possibility of unobserved confounders and ambiguity in the direction of causality. Here, we propose a method that circumvents these issues and apply it to genomic data from 829 prostate cancer patients. We identify several genetic alterations that drive divergence as well as others that prevent this transition, locking tumours into one trajectory. Further analysis reveals that these genetic alterations may cause each other, implying a positive-feedback loop that accelerates divergence. Our findings provide insights into how cancer subtypes emerge and offer a foundation for genomic surveillance strategies aimed at monitoring the progression of prostate cancer. © 2025, CC BY-NC-SA.

关键词： Lung cancer

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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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Scalable algorithms for learning high-dimensional linear mixed models

arXiv

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arXiv 2018年

作者： Tan, Zilong Roche, Kimberly Zhou, Xiang Mukherjee, Sayan Department of Computer Science Duke University Program in Computational Biology & Bioinformatics Duke University Department of Biostatistics Center for Statistical Genetics University of Michigan Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University

Linear mixed models (LMMs) are used extensively to model dependecies of observations in linear regression and are used extensively in many application areas. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends at least linearly on the dimension p of the covariates, and often use heuristics that do not offer theoretical guarantees. We present scalable algorithms for learning high-dimensional LMMs with sublinear computational complexity dependence on p. Key to our approach are novel dual estimators which use only kernel functions of the data, and fast computational techniques based on the subsampled randomized Hadamard transform. We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation. Finally, we complement the theory with experiments on large synthetic and real data. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks

Modeling Global Dynamics from Local Snapshots with Deep Gene...

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International Conference on Sampling Theory and Applications (SampTA)

作者： Scott Gigante David van Dijk Kevin R. Moon Alexander Strzalkowski Guy Wolf Smita Krishnaswamy Computational Biology and Bioinformatics Program Yale University New Haven CT USA Department of Genetics Yale University New Haven CT USA Department of Mathematics and Statistics Utah State University Logan UT USA Department of Computer Science Princeton University Princeton NJ USA Department of Mathematics and Statistics Université de Montréal Montéal QC Canada

ISBN: (数字)9781728137414

ISBN: (纸本)9781728137421

Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, "snapshot" measurements that sample the output of the system are often available. In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is trained using samples of current and next-state pairs, and thus does not require longitudinal measurements. We show the advantage of DyMoN over shallow models such as Kalman filters and hidden Markov models, and other deep models such as recurrent neural networks in its ability to embody the dynamics (which can be studied via perturbation of the neural network) and generate longitudinal hypothetical trajectories. We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model.

关键词： Hidden Markov models Trajectory Neural networks Biological system modeling Data models Markov processes

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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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Paralog-divergent Features May Help Reduce Off-target Effects of Drugs:Hints from Glucagon Subfamily Analysis

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Genomics, Proteomics & bioinformatics 2017年第4期15卷 246-254页

作者： Zhining Sa Jingqi Zhou Yangyun Zou Zhixi Su Xun Gu State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology School of LifeSciencesFudan UniversityShanghai 200433China Department of Genetics Development and Cell BiologyProgram of Bioinformatics and Computational BiologyIowa State UniversityAmesIA 50011USA

Side effects from targeted drugs remain a serious conccrn. One reason is the nonselective binding of a drug to unintended proteins such as its paralogs, which arc highly homologous in sequences and have similar structures and drug-binding pockets. To identify targctablc differences between paralogs, we analyzed two types （type-I and type-ll） of functional divergence between two paralogs in the known target protein receptor family G-protein coupled receptors （GPCRs） at the amino acid level. Paralogous protein receptors in glucagon-like subfamily, glucagon receptor （GCGR） and glucagon-like peptide-I receptor （GLP-I R）, exhibit divergence in ligands and are clinically validated drug targets for type 2 diabetes. Our data showed that type-ll alnino acids were significantly enriched in the binding sites of antagonist MK-0893 to GCGR. which had a radical shift in physicochemical properties between GCGR and GLP-1R. We also examined the role of type-I amino acids between GCGR and GLP-IR. The divergent features between GCGR and GLP-I R paralogs may be helpful in their discrimination, thus enabling the identification of binding sites to reduce undesirable side effects and increase the target specificity of drugs.

关键词： Paralog Functional divergence Functional site Drug specificity Evolutionary conservation

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Author Correction: Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Nature computational science

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Nature computational science 2023年第9期3卷 805页

作者： Xi Chen Yuan Wang Antonio Cappuccio Wan-Sze Cheng Frederique Ruf Zamojski Venugopalan D Nair Clare M Miller Aliza B Rubenstein German Nudelman Alicja Tadych Chandra L Theesfeld Alexandria Vornholt Mary-Catherine George Felicia Ruffin Michael Dagher Daniel G Chawla Alessandra Soares-Schanoski Rachel R Spurbeck Lishomwa C Ndhlovu Robert Sebra Steven H Kleinstein Andrew G Letizia Irene Ramos Vance G Fowler Christopher W Woods Elena Zaslavsky Olga G Troyanskaya Stuart C Sealfon Center for Computational Biology Flatiron Institute New York NY USA. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. Department of Computer Science Princeton University Princeton NJ USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. Division of Infectious Diseases Department of Medicine Duke University School of Medicine Durham NC USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT USA. Battelle Memorial Institute Columbus OH USA. Division of Infectious Diseases Department of Medicine Weill Cornell Medicine New York NY USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. Department of Pathology and Department of Immunobiology Yale School of Medicine New Haven CT USA. Naval Medical Research Center Silver Spring MD USA. Precision Immunology Institute Icahn School of Medicine at Mount Sinai New York NY USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. elena.zaslavsky@mssm.edu. Center for Computational Biology Flatiron Institute New York NY USA. ogt@genomics.princeton.edu. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Computer Science Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. stuart.sealfon@mssm.edu.

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