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Bioinformatics Advance Access published online on March 1, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl074
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received December 9, 2005
Revised February 13, 2006
Accepted February 24, 2006

Article

GALGO: an R package for multivariate variable selection using genetic algorithms

Victor Trevino 1 and Francesco Falciani 1 *

1 School of Biosciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom

* To whom correspondence should be addressed.
Francesco Falciani, E-mail: f.falciani{at}bham.ac.uk


  Abstract

Summary: The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages to perform univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.

Availability: GALGO, along with supplementary information, can be downloaded from: http://www.bip.bham.ac.uk/bioinf/galgo.html.

Associate Editor: Alfonso Valencia
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