Biomarker selection and sample prediction for multi-category disease on MALDI-TOF data.
BACKGROUND
Diseases normally progress through several stages. Therefore, biomarkers corresponding to each stage may exist. 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 an 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 this study, a liver cancer matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) dataset was used, which comprises 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.


BACKGROUND
Supplementary materials are available at http://visionlab.uta.edu/biomarker/bioinfo.htm.
Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA.
Jung Hun
JH
Oh
Young Bun
YB
Kim
Prem
P
Gurnani
Kevin P
KP
Rosenblatt
Jean X
JX
Gao
D014408:Biomarkers, Tumor / Q000032:analysis* / Q000737:chemistry*; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis / Q000378:metabolism*; D018450:Disease Progression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008103:Liver Cirrhosis / Q000175:diagnosis / Q000378:metabolism*; D008113:Liver Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D014408:Biomarkers, Tumor