A robust meta-classification strategy for cancer detection from MS data.
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).
Bhanot
Gyan
G
Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, USA.
Alexe
Gabriela
G
Venkataraghavan
Babu
B
Levine
Arnold J
AJ
D014408:Biomarkers, Tumor / Q000378:metabolism*; D016022:Case-Control Studies; D003661:Decision Support Techniques; D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D011379:Prognosis; D011467:Prostate / Q000378:metabolism; D011470:Prostatic Hyperplasia / Q000175:diagnosis*; D011471:Prostatic Neoplasms / Q000145:classification* / Q000175:diagnosis*; D040901:Proteomics; D012015:Reference Standards; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D014408:Biomarkers, Tumor