Inorder to identify the biologically relevant gene module and also which are the genes responsible for causing a disease,we use Biclustering technique which is also a useful co-clustering technique.In this paper, we present a exceptional method to state specific gene modules and also functionally related gene modules which are the reason for disease causing ,by applying a genetic algorithm to genetic data which is in the form of microarray data. To detect these differentially expressed gene modules, the anticipated method finds biclusters in which genes are overexpressed or under expressed, and also which  are differentially-expressed in the samples of genetic data. Inorder to get the differentially expressed we perform three steps in which we use K means alogorithm for clustering and Cheng and Chruch algorithm for biclustering. As to overcome the drawbacks in clustering  we use Biclustering technique which reduce redundancy in the data.The ensuing gene modules uncover preferable exhibitions over near techniques in the GO (Gene Ontology) term enhancement test and an analyzed association between gene modules and infection.



Bioinformatics, Raw data, analysing, Preprocessing, Clustering, Bi-clustering

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