Larry Smith, Lorraine K. Tanabe, et al.
Genome Biology
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). © 2006 Wiley-VCH Verlag GmbH & Co. KGaA.
Larry Smith, Lorraine K. Tanabe, et al.
Genome Biology
Toby G. Rossman, Ekaterina I. Goncharova, et al.
Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis
Gowri Nayar, Edward Seabolt, et al.
Scientific Reports
Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics