Journal Article

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

Melanie Osl, Stephan Dreiseitl, Bernhard Pfeifer, Klaus Weinberger, Helmut Klocker, Georg Bartsch, Georg Schäfer, Bernhard Tilg, Armin Graber and Christian Baumgartner

in Bioinformatics

Volume 24, issue 24, pages 2908-2914
Published in print December 2008 | ISSN: 1367-4803
Published online September 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn506
A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry

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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 as 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 (IG) and ReliefF (RF)] 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 IG and RF. 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 following http://biomed.umit.at/page.cfm?pageid=516

Contact: melanie.osl@umit.at

Journal Article.  5167 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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