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Functional annotation of chemical libraries across diverse biological processes (vol 13, pg 982, 2017)

NATURE CHEMICAL BIOLOGY 2017年第12期13卷 1286-1286页

作者： Piotrowski, Jeff S. Li, Sheena C. Deshpande, Raamesh Simpkins, Scott W. Nelson, Justin Yashiroda, Yoko Barber, Jacqueline M. Safizadeh, Hamid Wilson, Erin Okada, Hiroki Gebre, Abraham A. Kubo, Karen Torres, Nikko P. LeBlanc, Marissa A. Andrusiak, Kerry Okamoto, Reika Yoshimura, Mami DeRango-Adem, Eva van Leeuwen, Jolanda Shirahige, Katsuhiko Baryshnikova, Anastasia Brown, Grant W. Hirano, Hiroyuki Costanzo, Michael Andrews, Brenda Ohya, Yoshikazu Osada, Hiroyuki Yoshida, Minoru Myers, Chad L. Boone, Charles RIKEN Center for Sustainable Resource Science Wako Saitama Japan Department of Computer Science and Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Electrical and Computer Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Integrated Biosciences Graduate School of Frontier Sciences University of Tokyo Kashiwa Chiba Japan Donnelly Centre University of Toronto Toronto Ontario Canada Institute of Molecular and Cellular Biosciences Center for Epigenetic Disease University of Tokyo Bunkyo-ku Tokyo Japan Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Biochemistry University of Toronto Toronto Ontario Canada.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

关键词： Chemical genetics High-throughput screening Networks and systems biology Target identification

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Severe Congenital Neutropenia-Associated Mutations Reveal Stage-Specific Gfi1-Dependent Checkpoints in Myeloid Development

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BLOOD 2017年 130卷 544-544页

作者： Muench, David Kappes, Dietmar J. Salomonis, Nathan Myers, Kasiani C. Grimes, Lee Molecular and Developmental Biology Graduate Program Cincinnati Children's Hospital Medical Center Cincinnati OH Fox Chase Cancer Center Blood Cell Development and Function Program Philadelphia PA Bioinformatics Cincinnati Children's Hospital Medical Center Cincinnati OH Cincinnati Children's Hospital Cincinnati OH Division of Immunobiology Cincinnati Children's Hospital Medical Center Cincinnati OH

544

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Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

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Nature communications 2023年第1期14卷 3043页

作者： Jianxin Shi Kouya Shiraishi Jiyeon Choi Keitaro Matsuo Tzu-Yu Chen Juncheng Dai Rayjean J Hung Kexin Chen Xiao-Ou Shu Young Tae Kim Maria Teresa Landi Dongxin Lin Wei Zheng Zhihua Yin Baosen Zhou Bao Song Jiucun Wang Wei Jie Seow Lei Song I-Shou Chang Wei Hu Li-Hsin Chien Qiuyin Cai Yun-Chul Hong Hee Nam Kim Yi-Long Wu Maria Pik Wong Brian Douglas Richardson Karen M Funderburk Shilan Li Tongwu Zhang Charles Breeze Zhaoming Wang Batel Blechter Bryan A Bassig Jin Hee Kim Demetrius Albanes Jason Y Y Wong Min-Ho Shin Lap Ping Chung Yang Yang She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young-Chul Kim Neil E Caporaso Jiang Chang James Chung Man Ho Michiaki Kubo Yataro Daigo Minsun Song Yukihide Momozawa Yoichiro Kamatani Masashi Kobayashi Kenichi Okubo Takayuki Honda Dean H Hosgood Hideo Kunitoh Harsh Patel Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Hidehito Horinouchi Masahiro Tsuboi Ryuji Hamamoto Koichi Goto Yuichiro Ohe Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Megumi Hara Yuichiro Nishida Kenji Takeuchi Kenji Wakai Koichi Matsuda Yoshinori Murakami Kimihiro Shimizu Hiroyuki Suzuki Motonobu Saito Yoichi Ohtaki Kazumi Tanaka Tangchun Wu Fusheng Wei Hongji Dai Mitchell J Machiela Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Gee-Chen Chang Ying-Huang Tsai Kuan-Yu Chen Ming-Shyan Huang Wu-Chou Su Yuh-Min Chen Adeline Seow Jae Yong Park Sun-Seog Kweon Kun-Chieh Chen Yu-Tang Gao Biyun Qian Chen Wu Daru Lu Jianjun Liu Ann G Schwartz Richard Houlston Margaret R Spitz Ivan P Gorlov Xifeng Wu Ping Yang Stephen Lam Adonina Tardon Chu Chen Stig E Bojesen Mattias Johansson Angela Risch Heike Bickeböller Bu-Tian Ji H-Erich Wichmann David C Christiani Gadi Rennert Susanne Arnold Paul Brennan James McKay John K Field Sanjay S Shete Loic Le Marchand Geoffrey Liu Angeline Andrew Lambertus A Kiemeney Shan Zienolddiny-Narui Kjell Grankvist Mikael Johansson Angela Cox Fiona Taylor Jian-Min Yuan Philip Lazarus Matthew B Schabath Melinda C Aldrich Hyo-Sung Jeon Shih Sheng Jiang Jae Sook Sung C Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. jianxin.shi@nih.gov. Division of Genome Biology National Cancer Research Institute Tokyo Japan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Division of Cancer Epidemiology and Prevention Aichi Cancer Center Research Institute Nagoya Japan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Jiangsu Key Lab of Cancer Biomarkers Prevention and Treatment Collaborative Innovation Center for Cancer Medicine Nanjing Medical University Nanjing China. Prosserman Centre for Population Health Research Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada. Department of Epidemiology and Biostatistics National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Tianjin China. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology Cancer Institute and Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Department of Epidemiology School of Public Health China Medical University Shenyang China. Department of Clinical Epidemiology and Center of Evidence Based Medicine The First Hospital of China Medical University Shenyang China. Department of Oncology Shandong Cancer Hospital and Institute Shandong Academy of Medical Sciences Jinan China. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China. S

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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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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Innovative computational advances for disease outbreaks

Innovative computational advances for disease outbreaks

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World Congress on Engineering 2016, WCE 2016

作者： Gebreyesus, Kidane Desta Chang, Chuan-Hsiung Institute of Information Science Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Center for Systems and Synthetic Biology Institute of Biomedical Informatics National Yang-Ming University Taipei 112 Taiwan

ISBN: (纸本)9789881925305

SIR (susceptible, infectious, removed) epidemic models are commonly used in infectious disease study. These models use systems of differential equations to estimate epidemiological parameters. However, differential equations often suffer from numerical challenges. Here, we proposed simple, effective and robust innovative methods for estimating key infectious disease parameters. Our methods are reformed from the SIR models, by adjusting components of model compartments. We illustrated the methods using the recent large disease outbreaks, Ebola virus disease and Middle East respiratory syndrome Coronavirus.

关键词： Coronavirus

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Crowdsourcing annotation system of object counting dataset for deep learning algorithm

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IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： Tjeng Wawan Cenggoro Fidelson Tanzil Ayu Hidayah Aslamiah Ettikan Kandasamy Karuppiah Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 NVIDIA Corporation Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Deep Learning is currently the state-of-the-art technique for various Computer Vision tasks, including object counting. Despite of its high performance, Deep Learning requires a gigantic amount of training data to show its best result. Getting this massive data in reasonable time requires a proper strategy such as crowdsourcing. However, in case of object counting, we found no crowdsourcing system able to effectively collect necessary data. To tackle this problem, we develop a crowdsourcing system to annotate image for object counting dataset. This system is also equipped with validation system to ensure the quality of collected dataset.

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Doubly Penalized LASSO for Reconstruction of Biological Networks

Doubly Penalized LASSO for Reconstruction of Biological Netw...

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作者： Asadi, Behrang Maurya, Mano Ram Tartakovsky, Daniel M. Subramaniam, Shankar Departments of Bioengineering and Mechanical and Aerospace Engineering University of California San Diego Danaher Labs la Jolla Santa ClaraCA United States San Diego Supercomputer Center Department of Bioengineering University of California San Diego San DiegoCA United States Department of Mechanical Aerospace Engineering University of California San Diego San DiegoCA United States Department of Bioengineering Departments of Computer Science and Engineering Cellular and Molecular Medicine and the Graduate Program in Bioinformatics University of California San Diego San DiegoCA United States

Reconstruction of biological and biochemical networks is a crucial step in extracting knowledge and causal information from large biological data sets. This task is particularly challenging when dealing with time-series data from dynamic networks. We have developed a new method, called doubly penalized least absolute shrinkage and selection operator (DPLASSO), for the reconstruction of dynamic biological networks. Our method consists of two components: statistical significance testing of model coefficients and penalized/constrained optimization. A partial least squares (PLS) with statistical significance testing acts as a supervisory-level filter to extract the most informative components of the network from a data set. Then, LASSO with extra weights on the smaller parameters identified in the first layer is employed to retain the main predictors and to set the smallest coefficients to zero. We present two case studies to compare the relative performance of DPLASSO and LASSO in terms of several metrics, such as sensitivity, specificity, and accuracy. The first case study employs a synthetic data set;it demonstrates that DPLASSO substantially improves network reconstruction in terms of accuracy and specificity. The second case study relies on simulated data sets for cell division cycle of fission yeast and shows that DPLASSO overall outperforms LASSO and PLS in terms of sensitivity, specificity, and accuracy. Combining PLS with statistical significance testing and LASSO provides good performance with respect to several metrics. DPLASSO is well suited for reconstruction of networks with low and moderate density. For high-density networks, high specificity is desired, making PLS a more suitable approach. © 1963-2012 IEEE.

关键词： Least squares approximations

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Author Correction: Wiskott-Aldrich syndrome protein forms nuclear condensates and regulates alternative splicing

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Nature communications 2022年第1期13卷 5291页

作者： Baolei Yuan Xuan Zhou Keiichiro Suzuki Gerardo Ramos-Mandujano Mengge Wang Muhammad Tehseen Lorena V Cortés-Medina James J Moresco Sarah Dunn Reyna Hernandez-Benitez Tomoaki Hishida Na Young Kim Manal M Andijani Chongwei Bi Manching Ku Yuta Takahashi Jinna Xu Jinsong Qiu Ling Huang Christopher Benner Emi Aizawa Jing Qu Guang-Hui Liu Zhongwei Li Fei Yi Yanal Ghosheh Changwei Shao Maxim Shokhirev Patrizia Comoli Francesco Frassoni John R Yates Xiang-Dong Fu Concepcion Rodriguez Esteban Samir Hamdan Juan Carlos Izpisua Belmonte Mo Li Bioscience Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia. Gene Expression Laboratory Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Institute for Advanced Co-Creation Studies Graduate School of Engineering Science Osaka University Osaka Japan. Department of Cell Biology Scripps Research Institute La Jolla CA 92037 USA. The Waitt Advanced Biophotonics Core Facility Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Altos Labs Inc. 5510 Morehouse Drive Suite 300 San Diego CA 92121 USA. Laboratory of Biological Chemistry School of Pharmaceutical Sciences Wakayama Medical University 25-1 Shitibancho Wakayama Wakayama 640-8156 Japan. Next-generation sequencing core Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Life Science Center Tsukuba Advanced Research Alliance University of Tsukuba 1-1-1 Tennoudai Tsukuba Ibaraki 305-8577 Japan. Department of Cellular & Molecular Medicine University of California at San Diego La Jolla CA 92093 USA. Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. State Key Laboratory of Membrane Biology Institute of Zoology Chinese Academy of Sciences Beijing 100101 China. University of Southern California 1333 San Pablo Street MMR 618 Los Angeles CA 90033 USA. Ambys Medicines 131 Oyster Point Blvd. Suite 200 South San Francisco CA 94080 USA. Pediatric Hematology/Oncology and Cell Factory Fondazione IRCCS Policlinico San Matteo Pavia Italy. Department of Research Laboratories and Director of Center for Stem Cell and Cell Therapy Instituto G. Gaslini Children Hospital Scientific Institute 16147 Genova Italy. Bioscience Program Biological and Environmental Science and Engineering

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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