Clustering of a Number of Genes Affecting in Milk Production using Information Theory and Mutual Information

Information theory is a branch of mathematics. Information theory is used in genetic and bioinformatics analyses and can be used for many analyses related to the biological structures and sequences. Bio-computational grouping of genes facilitates genetic analysis, sequencing and structural-based analyses. In this study, after retrieving gene and exon DNA sequences affecting milk yield in dairy cattle, the entropy in orders one to four for each gene and eta exons was calculated. In order to extract gene distances, mutual information method was calculated. The results of mutual information of DNA and exon sequences were entered as input into 7 general clustering algorithms. In order to aggregate the results of clustering, AdaBoost algorithm was used. Finally, the results of AdaBoost algorithm were investigated by GeneMANIA prediction server to explore the results from gene annotation point of view. Integrated result of each clustering algorithm due to AdaBoost algorithm, which implied as gene tree, indicated that proposed method biologically grouped set of genes as it was proved by their gene annotation using GeneMANI. We believe that the proposed method might be used with other DNA based clustering competitive methods and therefore, it can be used to group set of genes in other species.