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Licensed Unlicensed Requires Authentication Published by De Gruyter September 27, 2005

Reverse Engineering Galactose Regulation in Yeast through Model Selection

  • Vesteinn Thorsson , Michael Hörnquist , Andrew F Siegel and Leroy Hood
From the journal Statistical Applications in Genetics and Molecular Biology
https://doi.org/10.2202/1544-6115.1118

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.

Keywords: Model selection; regression; AIC; BIC; MDL; Cp; Bootstrap; Lasso
Published Online: 2005-9-27

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

From the journal
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology
Volume 4 Issue 1
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