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Bioinformatics Advance Access originally published online on March 1, 2006
Bioinformatics 2006 22(9):1154-1156; 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

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

Victor Trevino and Francesco Falciani *

School of Biosciences, University of Birmingham Birmingham, B15 2TT, UK

*To whom correspondence should be addressed.

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 for performing 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 can be downloaded from http://www.bip.bham.ac.uk/bioinf/galgo.html

Contact: vtrevino{at}itesm.mx; f.falciani{at}bham.ac.uk

Supplementary information: Supplementary data are available at http://www.bip.bham.ac.uk/bioinf/galgo.html

Received on December 9, 2005; revised on February 13, 2006; accepted on February 24, 2006

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