Hybrid Genetic Algorithm and Simulated Annealing for Clustering Microarray Gene Expression data

• Published in: Journal of physics. Conference series Vol. 1767; no. 1; pp. 12034 - 12044
• Main Authors: Pandi, M, Sivakumar, T, Senthil Madasamy, N, Sadhasivam, N
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2021
• Subjects: Arrays; Clustering; Deoxyribonucleic acid; DNA; Evolutionary algorithms; Gene expression; Genes; Genetic algorithms; Physics; Real numbers; Simulated annealing
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1767/1/012034

Gene expression is the process by which information in gene is used to create proteins. The gene expression studies generate large amount of data. These data, referred to as the gene expression matrix, represent the expression levels for thousands of genes recorded at a few time instances. A typical microarray experiment involves the hybridization of an mRNA molecule to the DNA template from which it is originated. Many DNA samples are used to construct an array. The amount of mRNA bound to each site on the array indicates the expression level of the various genes. This number may run in thousands. All the data is collected and a profile is generated for gene expression in the cell. Clustering is a process of partitioning a set of meaningful subclasses called clusters. Clustering is a key step in the analysis of gene expression data. Genetic Algorithms are a family of computational models inspired by evolution. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster center in feature space such that a similarity metric of resulting clusters is optimized. The chromosome which are represented as strings of real numbers, encode the centers of fixed number of clusters. The experiment results are demonstrated on real data sets and the performance of GA is evaluated in comparison with the state-of-the art algorithm K-Means with use of internal validation criteria.