Proteomics. 2006 Jan;6(2):592-604.

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

Bhanot G, Alexe G, Venkataraghavan B, Levine AJ.

Source

Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, USA.

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).

PMID:: 16342141; [PubMed - indexed for MEDLINE]

Publication Types, MeSH Terms, Substances

Publication Types

Research Support, Non-U.S. Gov't

MeSH Terms

Substances

Tumor Markers, Biological

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Full Text Sources

Medical

Prostate Cancer - MedlinePlus Health Information

Supplemental Content

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