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


BACKGROUND
The algorithm can be downloaded from the following http://biomed.umit.at/page.cfm?pageid=516.
Osl
Melanie
M
Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at
Dreiseitl
Stephan
S
Pfeifer
Bernhard
B
Weinberger
Klaus
K
Klocker
Helmut
H
Bartsch
Georg
G
Schäfer
Georg
G
Tilg
Bernhard
B
Graber
Armin
A
Baumgartner
Christian
C
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000097:blood*; D015331:Cohort Studies; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D053719:Tandem Mass Spectrometry
D014408:Biomarkers, Tumor