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A Biclustering Based Classification Framework for Cancer Diagnosis and Prognosis

  • Author(s) / Creator(s)
  • 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. | TRID-ID TR08-07

  • Date created
    2008
  • Subjects / Keywords
  • Type of Item
    Report
  • DOI
    https://doi.org/10.7939/R3HQ3S50N
  • License
    Attribution 3.0 International