Reverse Engineering Galactose Regulation in Yeast through Model Selection

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Reverse Engineering Galactose Regulation in Yeast through Model Selection

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dc.contributor.author Thorsson, Vesteinn en_US
dc.contributor.author Hornquist, Michael en_US
dc.contributor.author Siegel, Andrew F. en_US
dc.contributor.author Hood, Leroy en_US
dc.date.accessioned 2009-12-15T21:00:58Z
dc.date.available 2009-12-15T21:00:58Z
dc.date.issued 2005 en_US
dc.identifier.citation Thorsson, Vesteinn; Hörnquist, Michael; Siegel, Andrew F.; and Hood, Leroy (2005) "Reverse Engineering Galactose Regulation in Yeast through Model Selection," Statistical Applications in Genetics and Molecular Biology: Vol. 4 : Iss. 1, Article 28. en_US
dc.identifier.other 10.2202/1544-6115.1118 en_US
dc.identifier.uri http://www.bepress.com/sagmb/vol4/iss1/art28 en_US
dc.identifier.uri http://hdl.handle.net/1773/15524
dc.description.abstract We examine the application of statistical model selection methods to reverse-engineering the control of galactose utilization in yeast from DNA microarray experiment data. In these experiments, relationships among gene expression values are revealed through modifications of galactose sugar level and genetic perturbations through knockouts. For each gene variable, we select predictors using a variety of methods, taking into account the variance in each measurement. These methods include maximization of log-likelihood with Cp, AIC, and BIC penalties, bootstrap and cross-validation error estimation, and coefficient shrinkage via the Lasso. en_US
dc.description.sponsorship STINT, CENIIT, VR, CTS, NIH en_US
dc.language.iso en_US en_US
dc.title Reverse Engineering Galactose Regulation in Yeast through Model Selection en_US
dc.type Article en_US


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