Bioinformatics Advance Access originally published online on January 19, 2007
Bioinformatics 2007 23(5):619-626; doi:10.1093/bioinformatics/btl678
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Peak selection from MALDI-TOF mass spectra using ant colony optimization
1Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057 USA, 2Critical Care Medicine, NIH, Bethesda, MD 20792 USA, 3Clinical Chemistry Service, Department of Laboratory Medicine, NIH, Bethesda, MD 20892 USA and 4Viral Hepatitis Research Laboratory, NHTMRI, Cairo, Egypt
*To whom correspondence should be addressed.
| Abstract |
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Motivation: Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks.
Results: The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method.
Availability: Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htm
Contact: hwr{at}georgetown.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on November 19, 2006; revised on December 20, 2006; accepted on January 5, 2007
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