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CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

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Nature immunology 2025年 2025 May 22页

作者： Bokai Zhu Sheng Gao Shuxiao Chen Yuchen Wang Jason Yeung Yunhao Bai Amy Y Huang Yao Yu Yeo Guanrui Liao Shulin Mao Zhenghui G Jiang Scott J Rodig Ka-Chun Wong Alex K Shalek Garry P Nolan Sizun Jiang Zongming Ma Ragon Institute of MGH MIT and Harvard Cambridge MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Massachusetts Institute of Technology Cambridge MA USA. Department of Statistics and Data Science The Wharton School University of Pennsylvania Philadelphia PA USA. Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Department of Computer Science City University of Hong Kong Hong Kong People's Republic of China. Dana-Farber Cancer Institute Boston MA USA. Center of Hepato-Pancreato-Biliary Surgery The First Affiliated Hospital of Sun Yat-sen University Guangzhou People's Republic of China. Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School Boston MA USA. Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA. Division of Gastroenterology/Liver Center Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA. Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA USA. Department of Pathology Stanford University Stanford CA USA. gnolan@stanford.edu. Broad Institute of MIT and Harvard Cambridge MA USA. sjiang3@bidmc.harvard.edu. Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. sjiang3@bidmc.harvard.edu. Dana-Farber Cancer Institute Boston MA USA. sjiang3@bidmc.harvard.edu. Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA. sjiang3@bidmc.harvard.edu. Department of Statistics and Data Science Yale University New Haven CT USA. zongming.ma@yale.edu.

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells' molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions.

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

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

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

关键词： Artificial intelligence

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Author Correction: Genetic drivers and cellular selection of female mosaic X chromosome loss

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Nature 2024年第8043期636卷 E7页

作者： Aoxing Liu Giulio Genovese Yajie Zhao Matti Pirinen Seyedeh M Zekavat Katherine A Kentistou Zhiyu Yang Kai Yu Caitlyn Vlasschaert Xiaoxi Liu Derek W Brown Georgi Hudjashov Bryan R Gorman Joe Dennis Weiyin Zhou Yukihide Momozawa Saiju Pyarajan Valdislav Tuzov Fanny-Dhelia Pajuste Mervi Aavikko Timo P Sipilä Awaisa Ghazal Wen-Yi Huang Neal D Freedman Lei Song Eugene J Gardner Vijay G Sankaran Aarno Palotie Hanna M Ollila Taru Tukiainen Stephen J Chanock Reedik Mägi Pradeep Natarajan Mark J Daly Alexander Bick Steven A McCarroll Chikashi Terao Po-Ru Loh Andrea Ganna John R B Perry Mitchell J Machiela Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland. liuaoxin@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. liuaoxin@***. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. liuaoxin@***. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. liuaoxin@***. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. liuaoxin@***. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. giulio@***. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. giulio@***. Department of Genetics Harvard Medical School Boston MA USA. giulio@***. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland. Department of Public Health University of Helsinki Helsinki Finland. Department of Mathematics and Statistics University of Helsinki Helsinki Finland. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. Cardiovascular Research Center Massachusetts General Hospital Boston MA USA. Department of Ophthalmology Massachusetts Eye and Ear Harvard Medical School Boston MA USA. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Department of Medicine Queen's University Kingston Ontario Canada. Laboratory for Statistical and Translational Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Cancer Prevention Fellowship Program Division of Cancer Prevention National Cancer Institute Rockville MD USA. Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia. Center for Data and Computational Sciences (C-DACS) VA Cooperative Studies Program VA Boston Health

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OS12.7.A Characterization of intra-tumoral heterogeneity and differential immune activation during malignant progression of meningiomas on single cell level

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Neuro-Oncology 2021年第SUPPLEMENT_2期23卷 ii15-ii16页

作者： Blume, C Dogan, H Schweizer, L Wick, W Weller, M Mann, M Kalamarides, M von Deimling, A Schlesner, M Sahm, F CCU Neuropathology German Cancer Research Centre (DKFZ) Heidelberg Germany Dept. of Neuropathology University Hospital Heidelberg Heidelberg Germany Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany German Cancer Research Centre (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Department of Neurology Clinical Neuroscience Center University Hospital and University of Zürich Zürich Switzerland NNF Center for Protein Research Faculty of Health Sciences University of Copenhagen Copenhagen Denmark Sorbonne Université and Department of Neurosurgery Pitié Salpêtrière Hospital Paris France Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

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OPTIMADE, an API for exchanging materials data

arXiv

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arXiv 2021年

作者： Andersen, Casper W. Armiento, Rickard Blokhin, Evgeny Conduit, Gareth J. Dwaraknath, Shyam Evans, Matthew L. Fekete, Ádám Gopakumar, Abhijith Gražulis, Saulius Merkys, Andrius Mohamed, Fawzi Oses, Corey Pizzi, Giovanni Rignanese, Gian-Marco Scheidgen, Markus Talirz, Leopold Toher, Cormac Winston, Donald Aversa, Rossella Choudhary, Kamal Colinet, Pauline Curtarolo, Stefano Di Stefano, Davide Draxl, Claudia Er, Suleyman Esters, Marco Fornari, Marco Giantomassi, Matteo Govoni, Marco Hautier, Geoffroy Hegde, Vinay Horton, Matthew K. Huck, Patrick Huhs, Georg Hummelshøj, Jens Kariryaa, Ankit Kozinsky, Boris Kumbhar, Snehal Liu, Mohan Marzari, Nicola Morris, Andrew J. Mostofi, Arash A. Persson, Kristin A. Petretto, Guido Purcell, Thomas Ricci, Francesco Rose, Frisco Scheffler, Matthias Speckhard, Daniel Uhrin, Martin Vaitkus, Antanas Villars, Pierre Waroquiers, David Wolverton, Chris Wu, Michael Yang, Xiaoyu École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Materials Design and Informatics Unit Department of Physics Chemistry and Biology Linköping University Sweden Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Theory of Condensed Matter Group Cavendish Laboratory 19 J.J. Thomson Avenue CambridgeCB3 0HE United Kingdom Lawrence Berkeley Labs BerkeleyCA United States Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Department of Physics King's College London London United Kingdom Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Institute of Biotechnology Life Science Center Vilnius University Saulétekio av. 7 VilniusLT-10257 Lithuania Institute of Computer Science Faculty of Mathematics and Informatics Vilnius University Naugarduko g. 24 VilniusLT-03225 Lithuania Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 Berlin14195 Germany Center for Autonomous Materials Design Duke University DurhamNC27708 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Humboldt-Universität zu Berlin Institut für Physik and IRIS Adlershof Berlin12489 Germany École Polytechnique Fédérale de Lausanne Sion1951 Switzerland Trieste34136 Italy Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Ansys 300 Rustat House 62 Clifton Rd CambridgeCB1 7EG United Kingdom De Zaale 20 Eindhoven5612 AJ Netherlands Department of Physics and Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Argonne National Laboratory LemontIL60439 United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Los AltosCA940

The Open databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification. © 2021, CC BY.

关键词： Specifications

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Genome-wide association study of long COVID

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Nature genetics 2025年 2025 May 21页

作者： Vilma Lammi Tomoko Nakanishi Samuel E Jones Shea J Andrews Juha Karjalainen Beatriz Cortés Heath E O'Brien Ana Ochoa-Guzman Brian E Fulton-Howard Martin Broberg Hele H Haapaniemi Masahiro Kanai Matti Pirinen Axel Schmidt Ruth E Mitchell Abdou Mousas Massimo Mangino Alicia Huerta-Chagoya Nasa Sinnott-Armstrong Elizabeth T Cirulli Marc Vaudel Alex S F Kwong Amit K Maiti Minttu M Marttila Daniel C Posner Alexis A Rodriguez Chiara Batini Francesca Minnai Anna R Dearman C A Robert Warmerdam Celia B Sequeros Thomas W Winkler Daniel M Jordan Raimonds Rešcenko Lorenzo Miano Jacqueline M Lane Ryan K Chung Beatriz Guillen-Guio Olivia C Leavy Laura Carvajal-Silva Kevin Aguilar-Valdés Erika Frangione Lindsay Guare Ekaterina Vergasova Eirini Marouli Pasquale Striano Ummu Afeera Zainulabid Ashutosh Kumar Hajar Fauzan Ahmad Ryuya Edahiro Shuhei Azekawa Shiuh-Wen Luoh Christian Erikstrup Ole B V Pedersen Jordan Lerner-Ellis Alicia Colombo Joseph J Grzymski Makoto Ishii Yukinori Okada Noam D Beckmann Meena Kumari Ralf Wagner Iris M Heid Catherine John Patrick J Short Per Magnus Laura Ansone Luca V C Valenti Sulggi A Lee Louise V Wain Ricardo A Verdugo Karina Banasik Frank Geller Lude H Franke Alexander Rakitko Emma L Duncan Alessandra Renieri Konstantinos K Tsilidis Rafael de Cid Ahmadreza Niavarani Erik Abner Teresa Tusié-Luna Shefali S Verma George Davey Smith Nicholas J Timpson Ravi K Madduri Kelly Cho Mark J Daly Andrea Ganna Eva C Schulte J Brent Richards Kerstin U Ludwig Michael Marks-Hultström Hugo Zeberg Hanna M Ollila Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland. Department of Human Genetics McGill University Montreal Quebec Canada. Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal Quebec Canada. Kyoto-McGill International Collaborative Program in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan. Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan. Research Fellow Japan Society for the Promotion of Science Tokyo Japan. Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA USA. Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. Genomes for Life-GCAT Lab CORE Program Germans Trias i Pujol Research Institute (IGTP) Badalona Spain. Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) Barcelona Spain. Sano Genetics Limited London UK. Unidad de Biología Molecular y Medicina Genómica Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York City NY USA. Broad Institute Cambridge MA USA. Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA USA. Department of Mathematics and Statistics University of Helsinki Helsinki Finland. Department of Public Health University of Helsinki Helsinki Finland. Institute of Human Genetics University of Bonn School of Medicine and University Hospital Bonn Bonn Germany. MRC Integrative Epidemiology Unit University of Bristol Bristol

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4's role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

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Author Correction: A genomic mutational constraint map using variation in 76,156 human genomes

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Nature 2024年第7997期626卷 E1页

作者： Siwei Chen Laurent C Francioli Julia K Goodrich Ryan L Collins Masahiro Kanai Qingbo Wang Jessica Alföldi Nicholas A Watts Christopher Vittal Laura D Gauthier Timothy Poterba Michael W Wilson Yekaterina Tarasova William Phu Riley Grant Mary T Yohannes Zan Koenig Yossi Farjoun Eric Banks Stacey Donnelly Stacey Gabriel Namrata Gupta Steven Ferriera Charlotte Tolonen Sam Novod Louis Bergelson David Roazen Valentin Ruano-Rubio Miguel Covarrubias Christopher Llanwarne Nikelle Petrillo Gordon Wade Thibault Jeandet Ruchi Munshi Kathleen Tibbetts Anne O'Donnell-Luria Matthew Solomonson Cotton Seed Alicia R Martin Michael E Talkowski Heidi L Rehm Mark J Daly Grace Tiao Benjamin M Neale Daniel G MacArthur Konrad J Karczewski Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. siwei@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. siwei@***. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Division of Medical Sciences Harvard Medical School Boston MA USA. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Data Science Platform Broad Institute of MIT and Harvard Cambridge MA USA. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. Division of Genetics and Genomics Boston Children's Hospital Boston MA USA. Richards Lab Lady Davis Institute Montreal Quebec Canada. Broad Institute of MIT and Harvard Cambridge MA USA. Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA. Institute for Molecular Medicine Finland (FIMM) Helsinki Finland. Centre for Population Genomics Garvan Institute of Medical Research and UNSW Sydney Sydney New South Wales Australia. Centre for Population Genomics Murdoch Children's Research Institute Melbourne Victoria Australia. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. konradk@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. konradk@***. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. konradk@***.

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

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arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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