Recursive Memetic Algorithm for gene selection in microarray data. (February 2019)

• Record Type: Journal Article
• Authors: Ghosh, Manosij; Begum, Shemim; Sarkar, Ram; Chakraborty, Debasis; Maulik, Ujjwal
• Is Part Of: Expert systems with applications. Volume 116(2019)
• Page Start: 172
• Page End: 185
• Publication Date: 2019-02
• Subjects: Recursive memetic algorithm -- Gene selection -- Microarry data -- Biomarker -- Cancer classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.06.057
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Development of a gene selection algorithm for identification of biomarkers from microarray data. Application of the method on seven widely used datasets. Validation of the genes obtained here using different metrics such as box-plots, heat maps among others. Reporting of biological significance of the genes through Gene Ontology and KEGG pathways. Citation of work showing obtained genes' status as biomarkers. Abstract: Feature selection algorithm contributes a lot in the domain of medical diagnosis. Choosing a small subset of genes that enable a classifier to predict the presence or type of disease accurately is a difficult optimisation problem due to the size of the microarray data. The dual task of achieving higher accuracy and a small number of features makes it a challenging research problem. In our work, we have developed a Recursive Memetic Algorithm (RMA) model for selection of genes. It is a variant of Memetic Algorithm (MA) and performs much better than MA as well as Genetic Algorithm (GA). RMA has been applied on seven microarray datasets namely, AMLGSE2191, Colon, DLBCL, Leukaemia, Prostate, MLL and SRBCT. Encouraging results obtained by the proposed model, reported in this article, are biologically validated with the use of Gene Oncology, KEGG pathways and heat maps.