Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

Abstract

In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper.

Heuristic computing with active set method for the nonlinear Rabinovich–Fabrikant model

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