PCP: a program for supervised classification of gene expression profiles.
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.


BACKGROUND
Free download at http://pcp.sourceforge.net
Buturović
Ljubomir J
LJ
San Francisco State University 1600 Holloway Avenue, San Francisco, CA 94132, USA. ljubomir@sfsu.edu
D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011381:Programming Languages; D012984:Software