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A robust meta‐classification strategy for cancer detection from MS data
Author(s) -
Bhanot Gyan,
Alexe Gabriela,
Venkataraghavan Babu,
Levine Arnold J
Publication year - 2006
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200500192
Subject(s) - prostate cancer , identification (biology) , computer science , cancer , proteomics , computational biology , noise (video) , artificial intelligence , data mining , pattern recognition (psychology) , biology , medicine , botany , biochemistry , gene , image (mathematics)
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).