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Biomarker Identification and Rule Extraction from Mass Spectral Serum Profiles

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8 Author(s)
Ressom, H.W. ; Lombardi Comprehensive Cancer Center, Georgetown Univ. Med. Center, Washington, DC ; Varghese, R.S. ; Orvisky, E. ; Drake, S.K.
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In this paper, we introduce a novel feature selection method that combines ant colony optimization (ACO) with support vector machine (SVM) to identify candidate biomarkers from mass spectral serum profiles. In addition, we present an innovative rule extraction algorithm that uses ACO to select accurate if-then rules for the classification of mass spectra. We applied the proposed feature selection and rule extraction methods to identify candidate biomarkers and extract if-then classification rules from MALDI-TOF spectra of enriched serum. The candidate biomarkers and the associated rules distinguished hepatocellular carcinoma patients from matched controls with high sensitivity and specificity

Published in:

Computational Intelligence and Bioinformatics and Computational Biology, 2006. CIBCB '06. 2006 IEEE Symposium on

Date of Conference:

28-29 Sept. 2006

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