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Bioinformatics Advance Access originally published online on November 8, 2005
Bioinformatics 2006 22(2):245-247; doi:10.1093/bioinformatics/bti760
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

PCP: a program for supervised classification of gene expression profiles

Ljubomir J. Buturovic

San Francisco State University 1600 Holloway Avenue, San Francisco, CA 94132, USA

Summary: PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms.

Availability: Free download at http://pcp.sourceforge.net

Contact: ljubomir{at}sfsu.edu

Received on August 10, 2005; revised on September 18, 2005; accepted on November 2, 2005

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