A robust meta-classification strategy for cancer detection from MS data.

Gyan Bhanot; Gabriela Alexe; Babu Venkataraghavan; Arnold J Levine

doi:10.1002/pmic.200500192

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A robust meta-classification strategy for cancer detection from MS data.

Gyan Bhanot·Gabriela Alexe·Babu Venkataraghavan·Arnold J Levine

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Abstract

We propose a novel method for phenotype identification involving a stringent noise analysis and filtering procedure followed by combining the results of several machine learning tools to produce a robust predictor. We illustrate our method on SELDI-TOF MS prostate cancer data (http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp). Our method identified 11 proteomic biomarkers and gave significantly improved predictions over previous analyses with these data. We were able to distinguish cancer from non-cancer cases with a sensitivity of 90.31% and a specificity of 98.81%. The proposed method can be generalized to multi-phenotype prediction and other types of data (e.g., microarray data).

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Biomarkers, Tumor Case-Control Studies Decision Support Techniques Diagnosis, Differential Humans Male Prognosis Prostate Prostatic Hyperplasia Prostatic Neoplasms Proteomics Reference Standards Sensitivity and Specificity Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Journal Article Research Support, Non-U.S. Gov't

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Gyan Bhanot, Gabriela Alexe, Babu Venkataraghavan, Arnold J Levine. 2006. A robust meta-classification strategy for cancer detection from MS data.. https://doi.org/10.1002/pmic.200500192

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