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Bioinformatics Advance Access published online on September 24, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn506
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry

Melanie Osl 1,*, Stephan Dreiseitl 1,3, Bernhard Pfeifer 1, Klaus Weinberger 4, Helmut Klocker 5, Georg Bartsch 5, Georg Schäfer 6, Bernhard Tilg 1, Armin Graber 2,4 and Christian Baumgartner 1

1Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria
2Institute for Bioinformatics, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria
3Department of Software Engineering, Upper Austria University of Applied Sciences, Hagenberg, Austria
4Biocrates Life Sciences, Innsbruck, Austria
5University Clinic for Urology, Innsbruck Medical University, Innsbruck, Austria
6Institute for Pathology, Innsbruck Medical University, Innsbruck, Austria

*To whom correspondence should be addressed. Melanie Osl, E-mail: melanie.osl{at}umit.at


  Abstract

Motivation: Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management.

Results: We developed a novel feature selection algorithm termed associative voting (AV) for identifying biomarker candidates in prostate cancer data measured via targeted metabolite profiling MS/MS analysis. We benchmarked our algorithm against two standard entropy-based and correlation-based feature selection methods (Information Gain and ReliefF) and observed that, on a variety of classification tasks in prostate cancer diagnosis, our algorithm identified subsets of biomarker candidates that are both smaller and show higher discriminatory power than the subsets identified by Information Gain and ReliefF. A literature study confirms that the highest-ranked biomarker candidates identified by AV have independently been identified as important factors in prostate cancer development.

Availability: The algorithm can be downloaded from the followingURL: http://biomed.umit.at/page.cfm?pageid=516

Contact: melanie.osl{at}umit.at

Associate Editor: Prof. Thomas Lengauer

Received on May 16, 2008; revised on September 3, 2008; accepted on September 22, 2008

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