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dc.contributor.authorThorsson, Vesteinnen_US
dc.contributor.authorHornquist, Michaelen_US
dc.contributor.authorSiegel, Andrew F.en_US
dc.contributor.authorHood, Leroyen_US
dc.date.accessioned2009-12-15T21:00:58Z
dc.date.available2009-12-15T21:00:58Z
dc.date.issued2005en_US
dc.identifier.citationThorsson, 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.other10.2202/1544-6115.1118en_US
dc.identifier.urihttp://www.bepress.com/sagmb/vol4/iss1/art28en_US
dc.identifier.urihttp://hdl.handle.net/1773/15524
dc.description.abstractWe 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.sponsorshipSTINT, CENIIT, VR, CTS, NIHen_US
dc.language.isoen_USen_US
dc.titleReverse Engineering Galactose Regulation in Yeast through Model Selectionen_US
dc.typeArticleen_US


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