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Bioinformatics Advance Access published online on November 2, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti738
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 15, 2005
Revised August 25, 2005
Accepted October 20, 2005

Article

A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer

Wensheng Zhang 1, Romdhane Rekaya 2*, and Keith Bertrand 1

1 Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602
2 Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602; Department of Statistics, University of Georgia, Athens, GA 30602

* To whom correspondence should be addressed.
Romdhane Rekaya, E-mail: rrekaya{at}uga.edu


  Abstract

Motivation: An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which it is seldom available in the majority of the cases. Thus, the supervised learning step has to be conducted with a statistical model that contemplates and handles potential mislabeling in the input data.

Results: A procedure for handling potential mislabeling among training samples in the prediction of disease subtypes using gene expression data was proposed. A real data-based simulation study about the estrogen receptor status (ER+/ER-) of breast cancer patients was conducted. The results demonstrated that when 1-4 training samples (N=30) were artificially mislabeled, the proposed method was able no only in correcting the ER status of mislabeled training samples, but more importantly, in predicting the ER status of validation samples as well as using "true" training data.

Availability: The programs (in Matlab) used for analysis are publicly available at: http://nce.ads.uga.edu/~romdhane.


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