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Variable importance-weighted Random Forests

Frontiers of Electrical and Electronic Engineering in China 2017年第4期5卷 338-351页

作者： Yiyi Liu Hongyu Zhao Department of Biostatistics School of Public Health Yale University New Haven CT 06511 USA Program of Computational Biology and Bioinformatics Yale University New Haven CT 06511 USA

Background： Random Forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination Random Forests was proposed that only uses features with the largest variable importance scores. Yet the performance of this method is not satisfying, possibly due to its rigid feature selection, and increased correlations between trees of forest. Methods： We propose variable importance-weighted Random Forests, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split from the randomly selected features. Results： We evaluate the performance of our method through comprehensive simulation and real data analyses, for both regression and classification. Compared to the standard Random Forests and the feature elimination Random Forests methods, our proposed method has improved performance in most cases. Conclusions： By incorporating the variable importance scores into the random feature selection step, our method can better utilize more informative features without completely ignoring less informative ones, hence has improved prediction accuracy in the presence of weak signals and large noises. We have implemented an R package ＂viRandomForests＂ based on the original R package ＂randomForest＂ and it can be freely downloaded from http：// ***/software.

关键词： Random Forests variable importance score classification regression

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A pathway-based kernel boosting method for sample classification using genomic data

arXiv

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arXiv 2018年

作者： Zeng, Li Yu, Zhaolong Zhao, Hongyu Department of Biostatistics Yale University New HavenCT06511 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States

The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify relevant pathways, but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classifica- tion loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites, and metastasis status. Our results show that PKB outperforms other methods, and identifies pathways relevant to the outcome variables. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Correction: Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer

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Clinical & experimental metastasis 2023年第4期40卷 373页

作者： Sophia M Orbach Michael D Brooks Yining Zhang Scott E Campit Grace G Bushnell Joseph T Decker Ryan J Rebernick Sriram Chandrasekaran Max S Wicha Jacqueline S Jeruss Lonnie D Shea Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. Department of Internal Medicine University of Michigan Ann Arbor MI USA. Department of Chemical Engineering University of Michigan Ann Arbor MI USA. Program in Chemical Biology University of Michigan Ann Arbor MI USA. Medical Science Training Program University of Michigan Ann Arbor MI USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Rogel Cancer Center University of Michigan Ann Arbor MI USA. Department of Surgery University of Michigan Ann Arbor MI USA. Department of Pathology University of Michigan Ann Arbor MI USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. ldshea@umich.edu. Department of Chemical Engineering University of Michigan Ann Arbor MI USA. ldshea@umich.edu. Rogel Cancer Center University of Michigan Ann Arbor MI USA. ldshea@umich.edu.

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Text mining systems biology: Turning the microscope back on the observer

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Current Opinion in Systems biology 2018年 11卷 117-122页

作者： Kong, Xiangmeng Gerstein, Mark B. Program in Computational Biology & Bioinformatics Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Molecular Biophysics & Biochemistry Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Computer Science Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States

In this review, we describe the relationship between systems biology and text mining. On the one hand, text mining functions as a practical tool for systems biology research, which integrates diverse sub-fields and takes an overall, systems-level view of biological phenomena. In this vein, various analyses have been done to recognize biological entities, construct networks of them (e.g. protein–protein interaction maps) and even find disease-associated genes directly from texts. On the other hand, text mining can also be applied to study systems biology itself, giving a ”distant-reading perspective” on the evolution of the field. For example, by examining changes in the frequencies of terms in systems biology publications, we can analyze trends in research focus and in the popularity of systems approaches in various subdomains (e.g., autism). Given these two uses of text mining for systems biology, we close with suggestions for adapting current publication formats for facilitating text mining and enabling its broader use. © 2018

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Integrating yeast chemical genomics and mammalian cell pathway analysis

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中国药理学报（英文版） 2019年第9期40卷 1245-1255页

作者： Fu-lai Zhou Sheena C Li Yue Zhu Wan-jing Guo Li-jun Shao Justin Nelson Scott Simpkins De-hua Yang Qing Liu Yoko Yashiroda Jin-biao Xu Yao-yue Fan Jian-min Yue Minoru Yoshida Tian Xia Chad L Myers Charles Boone Ming-wei Wang The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia MedicaChinese Academy of Sciences(CAS)Shanghai 201203China University of Chinese Academy of Sciences Beijing 100049China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Bioinformatics and Computational Biology Program University of Minnesota-Twin CitiesMinneapolisMinnesota 55455USA The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia MedicaChinese Academy of Sciences(CAS)Shanghai 201203China The State Key Laboratory of Drug Research Shanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Department of Biology The University of TokyoBunkyo-kuTokyo 1138657Japan Collaborative Research for Innovative Microbiology The University of TokyoBunkyo-kuTokyo 1138657Japan Department of Electronics and Information Engineering Huazhong University of Science and TechnologyWuhan 430074China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Donnelly Centre and Department of Molecular Genetics University of TorontoOntario M5S 3E1Canada

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces ***,we use yeast as a surrogate system for studying compounds that are active against metazoan ***-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target *** discover compounds that have the potential to function like therapeutic agents with known targets,we also analyzed a reference library of approved *** uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells,and new modulators with specific modes of action were *** analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

关键词： yeast chemical genomics tubulin cytoskeleton assembly autophagy Wnt/β-catenin signaling pathway

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O-GlcNAcylation destabilizes the active tetrameric PKM2 to promote the Warburg effect

O-GlcNAcylation destabilizes the active tetrameric PKM2 to p...

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中国生物化学与分子生物学会第十二届全国会员代表大会暨2018年全国学术会议

作者： Yang Wang Jia Liu Xin Jin Dapeng Zhang Dongxue Li Fengqi Hao Yunpeng Feng Shan Gu Fanlin Meng Miaomiao Tian Yi Zheng Ling Xin Xinbo Zhang Xue Han L.Aravind Min Wei Key Laboratory of Molecular Epigenetics of the Ministry of Education (MOE) Northeast Normal University Department of Biology and Program of Bioinformatics and Computational Biology Saint Louis University National Center for Biotechnology Information National Library of MedicineNational Institutes of Health

The Warburg effect,characterized by increased glucose uptake and lactate production,is a well-known universal across cancer-and other ***2,a splice isoform of the pyruvate kinase（PK） specifically expressed in these cells,serves as a major regulator of this metabolic reprogramming with an adjustable activity subjected to numerous allosteric effectors and posttranslational *** we have identified a novel posttranslational modification on PKM2,O-GlcNAcylation,which specifically targets Thr and Ser,residues of the region encoded by the alternatively spliced exon 10 in cancer *** show that PKM2 O-GlcNAcylation is upregulated in various types of human tumor cells and patient tumor *** modification destabilized the active tetrameric PKM2,reduced PK activity and led to nuclear translocation of *** also observed that the modification is associated with an increased glucose consumption and lactate production,and enhanced level of lipid and DNA synthesis,indicating that O-GlcNAcylation promotes the Warburg *** vivo experiments showed that blocking PKM2 O-GlcNAcylation attenuated tumor ***,we demonstrate that O-GlcNAcylation is a new regulatory mechanism for PKM2 in cancer cells and serves as a bridge between PKM2 and metabolic reprogramming typical of the Warburg effect.

关键词： PKM2 O-GlcNAcylation Warburg effect cancer metabolism

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Author Correction: Myeloid lineage enhancers drive oncogene synergy in CEBPA/CSF3R mutant acute myeloid leukemia

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Nature communications 2022年第1期13卷 3471页

作者： Theodore P Braun Mariam Okhovat Cody Coblentz Sarah A Carratt Amy Foley Zachary Schonrock Brittany M Curtiss Kimberly Nevonen Brett Davis Brianna Garcia Dorian LaTocha Benjamin R Weeder Michal R Grzadkowski Joey C Estabrook Hannah G Manning Kevin Watanabe-Smith Sophia Jeng Jenny L Smith Amanda R Leonti Rhonda E Ries Shannon McWeeney Cristina Di Genua Roy Drissen Claus Nerlov Soheil Meshinchi Lucia Carbone Brian J Druker Julia E Maxson Knight Cancer Institute Oregon Health & Science University Portland OR 97239 USA. Division of Hematology and Medical Oncology Oregon Health & Science University Portland OR 97239 USA. Knight Cardiovascular Institute Oregon Health & Science University Portland OR 97239 USA. Program in Molecular and Cellular Biology Oregon Health & Science University Portland OR 97239 USA. Computational Biology Program Oregon Health & Science University Portland OR 97239 USA. Division of Bioinformatics and Computational Biology Oregon Health & Science University Portland OR 97239 USA. Clinical Research Division Fred Hutchinson Cancer Research Center 1100 Fairview Ave N Seattle WA 98109 USA. MRC Molecular Haematology Unit MRC Weatherall Institute of Molecular Medicine John Radcliffe Hospital Headley Way Oxford OX3 9DS UK. Division of Pediatric Hematology/Oncology University of Washington Seattle WA 98195 USA. Department of Molecular and Medical Genetics Oregon Health & Science University Portland OR 97239 USA. Howard Hughes Medical Institute Portland OR USA. Knight Cancer Institute Oregon Health & Science University Portland OR 97239 USA. maxsonj@ohsu.edu. Division of Hematology and Medical Oncology Oregon Health & Science University Portland OR 97239 USA. maxsonj@ohsu.edu.

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Author Correction: A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

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Nature communications 2022年第1期13卷 7573页

作者： Wei Jiao Gurnit Atwal Paz Polak Rosa Karlic Edwin Cuppen Alexandra Danyi Jeroen de Ridder Carla van Herpen Martijn P Lolkema Neeltje Steeghs Gad Getz Quaid D Morris Lincoln D Stein Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada. Department of Molecular Genetics University of Toronto Toronto ON Canada. Vector Institute Toronto ON Canada. Broad Institute of MIT and Harvard Cambridge MA USA. Harvard Medical School Boston MA USA. Center for Cancer Research Massachusetts General Hospital Boston MA USA. Department of Oncological Sciences Icahn School of Medicine at Mount Sinai 15 1425 Madison Ave. New York NY USA. Bioinformatics Group Division of Molecular Biology Department of Biology Faculty of Science University of Zagreb Horvatovac 102a Zagreb Croatia. Hartwig Medical Foundation Science Park 408 Amsterdam The Netherlands. Center for Molecular Medicine and Oncode Institute University Medical Center Utrecht Utrecht The Netherlands. Center for Molecular Medicine University Medical Center Utrecht Utrecht The Netherlands. Radboud University Medical Center Nijmegen The Netherlands. Department of Medical Oncology Erasmus MC Cancer Institute University Medical Center Rotterdam Dr. Molewaterplein 40 3015 GD Rotterdam The Netherlands. Department of Medical Oncology The Netherlands Cancer Institute Plesmanlaan 121 1066 CX Amsterdam The Netherlands. Department of Pathology Massachusetts General Hospital Boston MA USA. University of Toronto Toronto ON Canada. Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada. lincoln.stein@***. Department of Molecular Genetics University of Toronto Toronto ON Canada. lincoln.stein@***.

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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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Formalizing the representation of immune exposures for human immunology studies 9

Formalizing the representation of immune exposures for human...

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9th International Conference on Biological Ontology, ICBO 2018

作者： Vita, Randi Overton, James A. Cheung, Kei-Hoi Kleinstein, Steven H. Peters, Bjoern Division for Vaccine Discovery San Diego Institute for Allergy and Immunology San DiegoCA United States Department of Emergency Medicine Yale Center for Medical Informatics Yale School of Medicine New HavenCT United States Department of Pathology Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT United States

Human immunology studies typically examine how immune exposures associated with vaccinations, infectious, allergic or autoimmune diseases, or transplantations perturb the immune system with the goal to develop diagnostic tools and therapeutic interventions. While there are established approaches to formally represent the experimental data generated in such studies, which often comprises gene expression data, flow cytometry data, or serology data, the description of the immune exposures themselves is not well standardized. We here present a formal approach to represent immune exposures at a high level of granularity. We capture the exposure process (e.g. 'vaccination' or 'occurrence of allergic disease'), exposure material (e.g. 'Tdap vaccine' or 'House dust mite'), and the associated disease name and stage (e.g. 'allergic rhinitis' and 'chronic'). This representation scheme has been used successfully in the IEDB and an extended version has been adopted by HIPC to capture studies in ImmPort. We are reporting here on this scheme, our ongoing attempts to map the terms used to existing ontologies, and the challenges encountered. © 2018 CEUR-WS.

关键词： Ontology

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