Hisashi Kashima, Yoshihiro Yamanishi, et al.
Bioinformatics
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.
Hisashi Kashima, Yoshihiro Yamanishi, et al.
Bioinformatics
Thomas L. Fabry, Haskell A. Reich
Biochemical and Biophysical Research Communications
Peter N. Ayittey, John S. Walker, et al.
Pflugers Archiv European Journal of Physiology
R. Langridge, M.P. Barnett, et al.
Journal of Molecular Biology