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Gene Subset Selection for Leukemia Classification Using Microarray Data


Mohamed Nisper Fathima Fajila*  


Cancer subtype identification is an active research field which helps diagnosis of various cancers with proper treatments. Leukemia is one such cancer with various subtypes. High throughput technologies such as Deoxyribo Nucleic Acid (DNA) microarray are highly active in the field of cancer detection and classification alternatively. Yet, precise analysis is important in microarray data applications. Gene selection techniques promote the microarray usage in the field of medicine. In this study, multi-objective evolutionary algorithm is used for gene subset selection in Leukemia classification. An initial redundant and irrelevant gene removal is followed by multi-objective evolutionary based gene subset selection. Gene subset selection highly influences the perfect classification. Thus, selecting the appropriate algorithm for subset selection is important. The performance of the proposed method is compared against the standard genetic algorithm and evolutionary algorithm. Three Leukemia microarray datasets were used to evaluate the performance of the proposed method. Perfect classification was achieved for all the datasets only with few significant genes using the proposed approach.


Classification, Gene Selection, Microarray, Multi-Objective Evolutionary Algorithm, Redundant and Irrelevant Gene, Significant Genes


Department of Mathematical Sciences, Faculty of Applied Sciences, South Eastern University of Sri Lanka, Sammanthurai

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