An extended Markov blanket approach to proteomic biomarker detection from high-resolution mass spectrometry data

Authors:
Jung Hun Oh;Prem Gurnani;John Schorge;Kevin P. Rosenblatt;Jean X. Gao
Affiliations:
Department of Computer Science and Engineering, University of Texas, Arlington, TX;PerkinElmer Life and Analytical Sciences, Waltham, MA;Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX;Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX;Department of Computer Science and Engineering, University of Texas, Arlington, TX
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2009

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Abstract

High-resolution matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has recently shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in finding such patterns is the large number of mass/charge peaks (features, biomarkers, data points) in a spectrum. To tackle this problem, we have developed methods for data preprocessing and biomarker selection. The preprocessing consists of binning, baseline correction, and normalization. An algorithm, extended Markov blanket, is developed for biomarker detection, which combines redundant feature removal and discriminant feature selection. The biomarker selection couples with support vector machine to achieve sample prediction from high-resolution proteomic profiles. Our algorithm is applied to recurrent ovarian cancer study that contains platinum-sensitive and platinum-resistant samples after treatment. Experiments show that the proposed method performs better than other feature selection algorithms. In particular, our algorithm yields good performance in terms of both sensitivity and specificity as compared to other methods.