Bioinformatics Advance Access published online on June 18, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn316
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Biomarker selection and sample prediction for multi-category disease on MALDI-TOF data
1Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA, 2PerkinElmer Life & Analytical Sciences, Waleham, MA 02451, USA, and 3Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX 77555, USA
*To whom correspondence should be addressed. Prof. Jean X. Gao, E-mail: gao{at}uta.edu
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Abstract |
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Motivation: Diseases normally progress in several stages. Therefore, there exist biomarkers corresponding to each stage. To deal with such a multi-category problem including sample stage prediction and biomarker selection, we propose methods for classification and feature selection. The proposed classification method is based on two schemes: error-correcting output coding (ECOC) and pairwise coupling (PWC). The final decision for a test sample prediction is the integration of these two schemes. The biomarker pattern for distinguishing each disease category from another one is achieved by the development of an Extended Markov Blanket (EMB) feature selection method.
Results: In the study, a liver cancer MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry (MS) data set was used, which consists of hepatocellular carcinoma (HCC), cirrhosis, and healthy spectra. Peak patterns were discovered for distinguishing pairwise categories among the three classes. Importance and reliability of individual peaks were presented by the measurements of certain weight values and frequencies. The classification capability of the proposed approach was compared with classical ECOC, random forest, Naive Bayes, and J48 methods.
Availability: Supplemental materials are available at http://visionlab.uta.edu/biomarker/bioinfo.htm
Contact: gao{at}uta.edu
Associate Editor: Dr. Jonathan Wren
Received on April 4, 2008; revised on May 25, 2008; accepted on June 12, 2008
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