Skip Navigation



Bioinformatics Advance Access published online on November 8, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti760
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
22/2/245    most recent
bti760v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Buturovic, L. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Buturovic, L. J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 10, 2005
Revised September 18, 2005
Accepted November 2, 2005

Applications note

PCP: a program for supervised classification of gene expression profiles

Ljubomir J. Buturovic 1*

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

* To whom correspondence should be addressed.
Ljubomir J. Buturovic, E-mail: ljubomir{at}sfsu.edu


  Abstract

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.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Toxicol SciHome page
R. S. Thomas, L. Pluta, L. Yang, and T. A. Halsey
Application of Genomic Biomarkers to Predict Increased Lung Tumor Incidence in 2-Year Rodent Cancer Bioassays
Toxicol. Sci., May 1, 2007; 97(1): 55 - 64.
[Abstract] [Full Text] [PDF]

Home page
 Toxicol SciHome page
R. S. Thomas, T. M. O'Connell, L. Pluta, R. D. Wolfinger, L. Yang, and T. J. Page
A Comparison of Transcriptomic and Metabonomic Technologies for Identifying Biomarkers Predictive of Two-Year Rodent Cancer Bioassays
Toxicol. Sci., March 1, 2007; 96(1): 40 - 46.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.