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    Sparse multitask regression for identifying common mechanism of response to therapeutic targets

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    Sparse multitask regression for ...
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    Genre
    Journal Article
    Date
    2010-06-01
    Author
    Zhang, K
    Gray, JW
    Parvin, B
    Subject
    Breast Neoplasms
    Female
    Gene Expression
    Gene Expression Profiling
    Genes, cdc
    Humans
    Regression Analysis
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5542
    
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    DOI
    10.1093/bioinformatics/btq181
    Abstract
    Motivation: 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.
    Citation to related work
    Oxford University Press (OUP)
    Has part
    Bioinformatics
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    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/5524
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