A Survey of Feature Selection Methods for the Analysis of Microarrays Data in Cancer
Keywords:
cancer identification, feature selection, microarray data, Support vector machinesAbstract
Cancerous gene selection and cancer identification are of great concern to biologists in interpreting the movements of genes in tissues at the molecular level. A Huge number of genes compared to fewer samples in microarray data pose a great difficulty in designing an appropriate machine learning model. To diagnose cancer and classify its types, obtaining significant genes analogous to cancer is crucial. Hence, it is a feature selection (FS) from gene expression data. Microarray datasets are noisy. Hence, significant FS algorithms are essential to select significant genes for classification. This paper depicts a review of FS methods, that have been reported in many journals to make use of microarray data-based cancer diagnosis. We hope this review will guide researchers to upgrade algorithmic developments in cancerous gene identification.
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