Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2673
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dc.contributor.authorTan, Mehmet-
dc.contributor.authorÖzgül, Ozan Fırat-
dc.contributor.authorBardak, Batuhan-
dc.contributor.authorEkşioğlu, Işıksu-
dc.contributor.authorSabuncuoğlu, S.-
dc.date.accessioned2019-12-25T14:02:00Z
dc.date.available2019-12-25T14:02:00Z
dc.date.issued2019-09
dc.identifier.citationTan, M., Özgül, O. F., Bardak, B., Ekşioğlu, I., and Sabuncuoğlu, S. (2019). Drug response prediction by ensemble learning and drug-induced gene expression signatures. Genomics, 111(5), 1078-1088.en_US
dc.identifier.issn0888-7543
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0888754318302416?via%3Dihub-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2673-
dc.description.abstractChemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in addition to cytotoxicity databases. These large sets of data provided an opportunity to apply machine learning methods to predict drug activity. However, due to the complexity of cancer drug mechanisms, none of the existing methods is perfect. In this paper, we propose a novel ensemble learning method to predict drug response. In addition, we attempt to use the drug screen data together with two novel signatures produced from the drug-induced gene expression profiles of cancer cell lines. Finally, we evaluate predictions by in vitro experiments in addition to the tests on data sets. The predictions of the methods, the signatures and the software are available from http://mtan.etu.edu.tr/drug-response-prediction/. © 2018 Elsevier Inc.en_US
dc.language.isoenen_US
dc.publisher Academic Press Inc.en_US
dc.relation.ispartofGenomicsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDrug signaturesen_US
dc.subjectcell line signaturesen_US
dc.subjectdrug response predictionen_US
dc.subjectensemble learningen_US
dc.titleDrug Response Prediction by Ensemble Learning and Drug-Induced Gene Expression Signaturesen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume111
dc.identifier.issue5
dc.identifier.startpage1078
dc.identifier.endpage1088
dc.relation.tubitakScientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [115E274]en_US
dc.authorid0000-0002-1741-0570-
dc.identifier.wosWOS:000485764600010en_US
dc.identifier.scopus2-s2.0-85049645304en_US
dc.institutionauthorTan, Mehmet-
dc.identifier.pmid31533900en_US
dc.identifier.doi10.1016/j.ygeno.2018.07.002-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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