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Permanent link (DOI): https://doi.org/10.7939/R3HQ3S50N

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A Biclustering Based Classification Framework for Cancer Diagnosis and Prognosis Open Access

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Author or creator
Malhotra, Baljeet
Lin, Guohui
Additional contributors
Subject/Keyword
cancer prognosis
biclustering
cancer diagnosis
Type of item
Computing Science Technical Report
Computing science technical report ID
TR08-07
Language
English
Place
Time
Description
Technical report TR08-07. In gene expression microarray data analysis, biclustering has been demonstrated to be one of the most effective methods for discovering gene expression patterns under various conditions. We present in this study a framework to take advantage of the homogeneously expressed genes in biclusters to construct a classifier for sample class membership prediction. Extensive experiments on 8 real cancer microarray datasets (4 diagnostic and 4 prognostic) show that our proposed classifier performed superior in both cancer diagnosis and prognosis, the latter of which was regarded quite difficult previously. Additionally, our results demonstrate that sample classification accuracy can serve as a good subjective quality measure for biclusters.
Date created
2008
DOI
doi:10.7939/R3HQ3S50N
License information
Creative Commons Attribution 3.0 Unported
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