Identification of Biclustering Algorithms for Gene Extraction

Many clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix have been proposed. This work explores the use of sub matrices, sub group of genes and sub groups of conditions to exhibit the genes highly correlated activities and identifies class of algorithms called biclustering suitable for gene extraction. Biclustering has also been widely used in fields such as information retrieval and data mining. In this comprehensive analysis a large number of existing approaches to biclustering has been examined and are classified in accordance with the type of biclusters that are identified, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.Biclustering approach facilitates an efficient output by considering only a subset of conditions when looking for similarity between genes.The subset of genes exhibits significant homogeneity within the subset of homogeneity criteria.Moreover,it is observed that biclustering techniques are also used for revealing sub matrices showing unique patterns.