Abstract
We present a novel method that combines ant colony optimization with support vector machines (ACO-SVM) to select candidate biomarkers from MALDI-TOF serum profiles of hepatocellular carcinoma (HCC) patients and matched controls. The method identified relevant mass points that achieve high sensitivity and specificity in distinguishing HCC patients from healthy individuals. The results indicate that the MALDI-TOF technology could provide the means to discover novel biomarkers for HCC.
Publication types
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Research Support, Non-U.S. Gov't
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Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
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Animals
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Ants
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Biomarkers / chemistry*
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Biomarkers, Tumor / chemistry*
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Carcinoma, Hepatocellular / diagnosis
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Carcinoma, Hepatocellular / metabolism*
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Humans
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Mass Spectrometry
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Peptides / chemistry
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Pheromones
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ROC Curve
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Reproducibility of Results
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Sensitivity and Specificity
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Signal Processing, Computer-Assisted
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Software
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Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / instrumentation*
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Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / methods*
Substances
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Biomarkers
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Biomarkers, Tumor
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Peptides
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Pheromones