{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T20:46:32Z","timestamp":1793047592749,"version":"4.5.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000196","name":"Canada Foundation for Innovation","doi-asserted-by":"publisher","award":["33440"],"award-info":[{"award-number":["33440"]}],"id":[{"id":"10.13039\/501100000196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"The Canadian Institutes of Health Research","doi-asserted-by":"crossref","award":["PJT-153073"],"award-info":[{"award-number":["PJT-153073"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002655","name":"Terry Fox Foundation","doi-asserted-by":"publisher","award":["201012TFF"],"award-info":[{"award-number":["201012TFF"]}],"id":[{"id":"10.13039\/501100002655","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Terry Fox New Frontiers Program Project","award":["1062"],"award-info":[{"award-number":["1062"]}]},{"name":"DFG Research Training Group GRK"},{"name":"1906"},{"name":"National Science and Engineering Research Council of Canada"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: (i) in the input space, the gene expression data due to difference in the basic biology, and (ii) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/hosseinshn\/AITL.<\/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\/btaa442","type":"journal-article","created":{"date-parts":[[2020,6,6]],"date-time":"2020-06-06T07:12:13Z","timestamp":1591427533000},"page":"i380-i388","source":"Crossref","is-referenced-by-count":54,"title":["AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics"],"prefix":"10.1093","volume":"36","author":[{"given":"Hossein","family":"Sharifi-Noghabi","sequence":"first","affiliation":[{"name":"School of Computing Science, Simon Fraser University , Burnaby, BC, Canada"},{"name":"Vancouver Prostate Centre , Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuman","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computing Science, Simon Fraser University , Burnaby, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Zolotareva","sequence":"additional","affiliation":[{"name":"International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes and Genome Informatics, Faculty of Technology and Center for Biotechnology, Bielefeld University , Bielefeld, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin C","family":"Collins","sequence":"additional","affiliation":[{"name":"Vancouver Prostate Centre , Vancouver, BC, Canada"},{"name":"University of British Columbia Department of Urologic Sciences, , Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Ester","sequence":"additional","affiliation":[{"name":"School of Computing Science, Simon Fraser University , Burnaby, BC, Canada"},{"name":"Vancouver Prostate Centre , Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"2024021913333063100_btaa442-B1","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.ccr.2007.11.014","article-title":"The extracellular matrix protein TGFBI induces microtubule stabilization and sensitizes ovarian cancers to paclitaxel","volume":"12","author":"Ahmed","year":"2007","journal-title":"Cancer Cell"},{"key":"2024021913333063100_btaa442-B2","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1038\/leu.2014.140","article-title":"Gene expression profile alone is inadequate in predicting complete response in multiple myeloma","volume":"28","author":"Amin","year":"2014","journal-title":"Leukemia"},{"key":"2024021913333063100_btaa442-B3","article-title":"Regularized learning for domain adaptation under label shifts","author":"Azizzadenesheli","year":"2019"},{"key":"2024021913333063100_btaa442-B4","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":"2024021913333063100_btaa442-B5","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1158\/1078-0432.CCR-09-1091","article-title":"Identification of markers of taxane sensitivity using proteomic and genomic analyses of breast tumors from patients receiving neoadjuvant paclitaxel and radiation","volume":"16","author":"Bauer","year":"2010","journal-title":"Clin. 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