Eran Eden, Doron Lipson, et al.
PLoS Computational 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.
Eran Eden, Doron Lipson, et al.
PLoS Computational Biology
M.P. Barnett, F.W. Birss, et al.
Molecular Physics
Vito Paolo Pastore, Thomas Zimmerman, et al.
Scientific Reports
Qing Zhong, Rui Sun, et al.
Life Science Alliance