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Because experimental verification of reactions between large cohort of patients and drugs is time-intensive, expensive and impractical, preclinical prediction model based on large-scale pharmacogenomic of cancer cell line is highly expected. However, most of the existing computational studies are primarily based on genomic profiles of cancer cell lines while ignoring relationships among genes and failing to capture functional similarity of cell lines.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we present a novel approach named NRL2DRP, which integrates protein\u2013protein interactions and captures similarity of cell lines\u2019 functional contexts, to predict drug responses. Through integrating genomic aberrations and drug responses information with protein\u2013protein interactions, we construct a large response-related network, where the neighborhood structure of cell line provides a functional context to its therapeutic responses. Representation vectors of cell lines are extracted through network representation learning method, which could preserve vertices\u2019 neighborhood similarity and serve as features to build predictor for drug responses. The predictive performance of NRL2DRP is verified by cross-validation on GDSC dataset and methods comparison, where NRL2DRP achieves AUC &amp;gt; 79% for half drugs and outperforms previous methods. The validity of NRL2DRP is also supported by its effectiveness on uncovering accurate novel relationships between cell lines and drugs. Lots of newly predicted drug responses are confirmed by reported experimental evidences.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code and documentation are available on https:\/\/github.com\/USTC-HIlab\/NRL2DRP.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty848","type":"journal-article","created":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T11:59:44Z","timestamp":1539086384000},"page":"1527-1535","source":"Crossref","is-referenced-by-count":61,"title":["A novel approach for drug response prediction in cancer cell lines via network representation learning"],"prefix":"10.1093","volume":"35","author":[{"given":"Jianghong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei AH230037, China"}]},{"given":"Ao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei AH230037, China"},{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei AH230037, China"}]},{"given":"Yongqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China"}]},{"given":"Xiangqian","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China"}]},{"given":"Minghui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei AH230037, China"},{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei AH230037, China"}]}],"member":"286","published-online":{"date-parts":[[2018,10,10]]},"reference":[{"key":"2023012806484828000_bty848-B1","doi-asserted-by":"crossref","first-page":"i455","DOI":"10.1093\/bioinformatics\/btw433","article-title":"Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization","volume":"32","author":"Ammad-Ud-Din","year":"2016","journal-title":"Bioinformatics"},{"key":"2023012806484828000_bty848-B2","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023012806484828000_bty848-B3","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1093\/bioinformatics\/btu557","article-title":"A novel feature-based approach to extract drug\u2013drug interactions from biomedical text","volume":"30","author":"Bui","year":"2014","journal-title":"Bioinformatics"},{"key":"2023012806484828000_bty848-B4","doi-asserted-by":"crossref","first-page":"1.","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: a library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. 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