{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:55:04Z","timestamp":1774670104536,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T00:00:00Z","timestamp":1571011200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009517","name":"PSL Research University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009517","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/jcboyd\/multi-cell-line or https:\/\/zenodo.org\/record\/2677923.<\/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\/btz774","type":"journal-article","created":{"date-parts":[[2019,10,8]],"date-time":"2019-10-08T15:33:06Z","timestamp":1570548786000},"page":"1607-1613","source":"Crossref","is-referenced-by-count":10,"title":["Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen"],"prefix":"10.1093","volume":"36","author":[{"given":"Joseph C","family":"Boyd","sequence":"first","affiliation":[{"name":"CBIO - Centre de Bio-Informatique, MINES ParisTech, PSL Research University , Paris 75006, France"},{"name":"Institut Curie , Paris Cedex 75248"},{"name":"INSERM U900 , Paris Cedex 75248"}]},{"given":"Alice","family":"Pinheiro","sequence":"additional","affiliation":[{"name":"Institut Curie , Paris Cedex 75248"},{"name":"INSERM U932 , Paris Cedex 75248, France"}]},{"given":"Elaine","family":"Del Nery","sequence":"additional","affiliation":[{"name":"Institut Curie , Paris Cedex 75248"},{"name":"INSERM U932 , Paris Cedex 75248, France"}]},{"given":"Fabien","family":"Reyal","sequence":"additional","affiliation":[{"name":"Institut Curie , Paris Cedex 75248"},{"name":"INSERM U932 , Paris Cedex 75248, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7419-7879","authenticated-orcid":false,"given":"Thomas","family":"Walter","sequence":"additional","affiliation":[{"name":"CBIO - Centre de Bio-Informatique, MINES ParisTech, PSL Research University , Paris 75006, France"},{"name":"Institut Curie , Paris Cedex 75248"},{"name":"INSERM U900 , Paris Cedex 75248"}]}],"member":"286","published-online":{"date-parts":[[2019,10,14]]},"reference":[{"key":"2023060910381453300_btz774-B1","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0076-6879(06)14024-0","article-title":"Compound classification using image-based cellular phenotypes","volume":"414","author":"Adams","year":"2006","journal-title":"Methods Enzymol"},{"key":"2023060910381453300_btz774-B2","author":"Ajakan","year":"2014"},{"key":"2023060910381453300_btz774-B3","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.cell.2010.04.033","article-title":"Cellular heterogeneity: do differences make a difference?","volume":"141","author":"Altschuler","year":"2010","journal-title":"Cell"},{"key":"2023060910381453300_btz774-B4","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","article-title":"A theory of learning from different domains","volume":"79","author":"Ben-David","year":"2010","journal-title":"Mach. 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