Reverse Engineering Galactose Regulation in Yeast through Model Selection

Thorsson Vesteinn, Hörnquist Michael, Siegel Andrew F and Hood Leroy
Additional contact information
Thorsson Vesteinn: Institute for Systems Biology, first two authors contributed equally
Hörnquist Michael: Linköping University, first two authors contributed equally
Siegel Andrew F: University of Washington
Hood Leroy: Institute for Systems Biology

Statistical Applications in Genetics and Molecular Biology, 2005, vol. 4, issue 1, 24

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.

Keywords: Model selection; regression; AIC; BIC; MDL; Cp; Bootstrap; Lasso (search for similar items in EconPapers)
Date: 2005
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1118 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:28

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1118

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().