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.

Submitted: December 15, 2004 · Accepted: July 1, 2005 · Published: September 27, 2005

Recommended 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.
Available at: http://www.bepress.com/sagmb/vol4/iss1/art28