Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6847
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dc.contributor.authorCostello, James C.-
dc.contributor.authorHeiser, Laura M.-
dc.contributor.authorGeorgii, Elisabeth-
dc.contributor.authorGönen, Mehmet-
dc.contributor.authorMenden, Michael P.-
dc.contributor.authorWang, Nicholas J.-
dc.contributor.authorBansal, Mukesh-
dc.contributor.authorAmmad-ud-din, Muhammad-
dc.contributor.authorHintsanen, Petteri-
dc.contributor.authorKhan, Suleiman A.-
dc.contributor.authorMpindi, John-Patrick-
dc.contributor.authorKallioniemi, Olli-
dc.contributor.authorHonkela, Antti-
dc.contributor.authorAittokallio, Tero-
dc.contributor.authorWennerberg, Krister-
dc.contributor.authorNCI-DREAM Community-
dc.contributor.authorKaraçalı, Bilge-
dc.contributor.authorCollins, James J.-
dc.contributor.authorGallahan, Dan-
dc.contributor.authorSinger, Dinah-
dc.contributor.authorSaez-Rodriguez, Julio-
dc.contributor.authorKaski, Samuel-
dc.contributor.authorGray, Joe W.-
dc.contributor.authorStolovitzky, Gustavo-
dc.date.accessioned2018-03-30T08:52:50Z-
dc.date.available2018-03-30T08:52:50Z-
dc.date.issued2014-12-
dc.identifier.citationCostello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., and Wang, N. J., ...Stolovitzky, G. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32(12), 1202-1212. doi:10.1038/nbt.2877en_US
dc.identifier.issn1546-1696-
dc.identifier.issn1087-0156-
dc.identifier.urihttp://doi.org/10.1038/nbt.2877-
dc.identifier.urihttp://hdl.handle.net/11147/6847-
dc.description.abstractPredicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.en_US
dc.description.sponsorshipMaGNeT grant 5U54CA121852-08; National Institutes of Health, National Cancer Institute (U54 CA 112970); Stand Up To Cancer-American Association for Cancer Research Dream Team Translational Cancer Research (SU2C-AACR-DT0409); Prospect Creek Foundation; Howard Hughes Medical Institute (HHMI); Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN) (251170--140057)en_US
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.relation.ispartofNature Biotechnologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGene expressionen_US
dc.subjectForecastingen_US
dc.subjectComputational modelsen_US
dc.subjectBiological pathwaysen_US
dc.subjectGenomic informationen_US
dc.titleA community effort to assess and improve drug sensitivity prediction algorithmsen_US
dc.typeArticleen_US
dc.authoridTR11527en_US
dc.institutionauthorKaraçalı, Bilge-
dc.departmentİzmir Institute of Technology. Electrical and Electronics Engineeringen_US
dc.identifier.volume32en_US
dc.identifier.issue12en_US
dc.identifier.startpage1202en_US
dc.identifier.endpage1212en_US
dc.identifier.wosWOS:000346156800022en_US
dc.identifier.scopus2-s2.0-84906549588en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1038/nbt.2877-
dc.identifier.pmid24880487en_US
dc.relation.doi10.1038/nbt.2877en_US
dc.coverage.doi10.1038/nbt.2877en_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.05. Department of Electrical and Electronics Engineering-
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
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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