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dc.creatorZhang, K
dc.creatorGray, JW
dc.creatorParvin, B
dc.date.accessioned2021-02-01T00:04:46Z
dc.date.available2021-02-01T00:04:46Z
dc.date.issued2010-06-01
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5524
dc.identifier.other20529943 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5542
dc.description.abstractMotivation: Molecular association of phenotypic responses is an important step in hypothesis generation and for initiating design of new experiments. Current practices for associating gene expression data with multidimensional phenotypic data are typically (i) performed one-to-one, i.e. each gene is examined independently with a phenotypic index and (ii) tested with one stress condition at a time, i.e. different perturbations are analyzed separately. As a result, the complex coordination among the genes responsible for a phenotypic profile is potentially lost. More importantly, univariate analysis can potentially hide new insights into common mechanism of response. Results: In this article, we propose a sparse, multitask regression model together with co-clustering analysis to explore the intrinsic grouping in associating the gene expression with phenotypic signatures. The global structure of association is captured by learning an intrinsic template that is shared among experimental conditions, with local perturbations introduced to integrate effects of therapeutic agents. We demonstrate the performance of our approach on both synthetic and experimental data. Synthetic data reveal that the multi-task regression has a superior reduction in the regression error when compared with traditional L1-and L2-regularized regression. On the other hand, experiments with cell cycle inhibitors over a panel of 14 breast cancer cell lines demonstrate the relevance of the computed molecular predictors with the cell cycle machinery, as well as the identification of hidden variables that are not captured by the baseline regression analysis. Accordingly, the system has identified CLCA2 as a hidden transcript and as a common mechanism of response for two therapeutic agents of CI-1040 and Iressa, which are currently in clinical use. Contact: b_parvin@lbl.gov. © The Author(s) 2010. Published by Oxford University Press.
dc.format.extenti97-i105
dc.language.isoen
dc.relation.haspartBioinformatics
dc.relation.isreferencedbyOxford University Press (OUP)
dc.rightsCC BY-NC
dc.subjectBreast Neoplasms
dc.subjectFemale
dc.subjectGene Expression
dc.subjectGene Expression Profiling
dc.subjectGenes, cdc
dc.subjectHumans
dc.subjectRegression Analysis
dc.titleSparse multitask regression for identifying common mechanism of response to therapeutic targets
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1093/bioinformatics/btq181
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-02-01T00:04:42Z
refterms.dateFOA2021-02-01T00:04:47Z


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