Abstract:Microarrays have now gone from obscurity to being almost ubiquitous in biological research.
At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to novel algorithms for analyzing changes in expression profiles. Microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. In this paper, we attempt a prediction scheme that combines kernalised fuzzy rough set (KFRS) method for feature (gene) selection with association rule based classification. Biomarkers are discovered employing three feature selection methods, including KFRS. The effectiveness of the proposed KFRS and association rule based classification combination on the microarray data sets is demonstrated, and the cancer biomarkers identified from miRNA data are reported.
To show the effectiveness of the proposed approach, we compare the performance of this technique with the Fuzzy Rough Set Attribute Reduction on Information Gain Ratio(FRS_GR), signal-to-noise ratio (SNR) and consistency based feature selection (CBFS) methods. Using four benchmark gene microarray datasets, we demonstrate experimentally that our proposed scheme can achieve significant empirical success and is biologically relevant for cancer diagnosis and drug discovery.