{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:05:25Z","timestamp":1784891125342,"version":"3.55.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2019,3,9]],"date-time":"2019-03-09T00:00:00Z","timestamp":1552089600000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Canadian Cancer Society Research Institute Innovation Grant","award":["703471"],"award-info":[{"award-number":["703471"]}]},{"name":"Natural Science and Engineering Research Council"},{"DOI":"10.13039\/501100000024","name":"Canadian Institute for Health Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Individualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce a unified probabilistic approach, Drug Response Variational Autoencoder (Dr.VAE), that simultaneously models both drug response in terms of viability and transcriptomic perturbations. Dr.VAE is a deep generative model based on variational autoencoders. Our experimental results showed Dr.VAE to do as well or outperform standard classification methods for 23 out of 26 tested Food and Drug Administration-approved drugs. In a series of ablation experiments we showed that the observed improvement of Dr.VAE can be credited to the incorporation of drug-induced perturbation effects with joint modeling of treatment sensitivity.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Processed data and software implementation using PyTorch (Paszke et al., 2017) are available at: https:\/\/github.com\/rampasek\/DrVAE.<\/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\/btz158","type":"journal-article","created":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T21:33:33Z","timestamp":1551908013000},"page":"3743-3751","source":"Crossref","is-referenced-by-count":165,"title":["Dr.VAE: improving drug response prediction via modeling of drug perturbation effects"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7527-1196","authenticated-orcid":false,"given":"Ladislav","family":"Ramp\u00e1\u0161ek","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Toronto , Toronto, ON, Canada"},{"name":"Genetics & Genome Biology, SickKids Research Institute , Toronto, ON, Canada"},{"name":"Vector Institute , Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Hidru","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Toronto , Toronto, ON, Canada"},{"name":"Genetics & Genome Biology, SickKids Research Institute , Toronto, ON, Canada"},{"name":"Vector Institute , Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9395-8450","authenticated-orcid":false,"given":"Petr","family":"Smirnov","sequence":"additional","affiliation":[{"name":"Vector Institute , Toronto, ON, Canada"},{"name":"Princess Margaret Cancer Centre, University Health Network , Toronto, ON, Canada"},{"name":"Department of Medical Biophysics, University of Toronto , Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7684-0079","authenticated-orcid":false,"given":"Benjamin","family":"Haibe-Kains","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Toronto , Toronto, ON, Canada"},{"name":"Vector Institute , Toronto, ON, Canada"},{"name":"Princess Margaret Cancer Centre, University Health Network , Toronto, ON, Canada"},{"name":"Department of Medical Biophysics, University of Toronto , Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2416-833X","authenticated-orcid":false,"given":"Anna","family":"Goldenberg","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Toronto , Toronto, ON, Canada"},{"name":"Genetics & Genome Biology, SickKids Research Institute , Toronto, ON, Canada"},{"name":"Vector Institute , Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,3,8]]},"reference":[{"key":"2023013108122132200_btz158-B1","first-page":"820","article-title":"Computational models for predicting drug responses in cancer research","volume":"18","author":"Azuaje","year":"2017","journal-title":"Brief. 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