A hybrid of Information gain and a Coati Optimization Algorithm for gene selection in microarray gene expression data classification.

Gene expression data has become an essen2al tool for cancer classifica2on because it provides substan2al insights into the underlying mechanisms of cancer progression. However, the high-dimensional nature of microarray gene expression data presents a significant challenge. This paper introduces a new method called IG-COA, which combines Informa2on Gain (IG) approach and Coa2 Op2miza2on Algorithm (COA), to iden2fy the biomarkers genes. COA is a recent algorithm that has not been previously examined for feature or gene selec2on, to the best of our knowledge. Firstly, the IG method is used because using COA directly on microarray datasets is ineffec2ve and can make it challenging to train a classifier accurately. Secondly, the COA algorithm is u2lized to select the op2mal subset of genes from the previously selected ones. The effec2veness of the suggested IG-COA method with a Support Vector Machine is tested on several microarray gene expression datasets, and it exceeds other state-of-the-art methods.