Biclustering for Microarray Data: A Short and Comprehensive Tutorial
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 10
Abstract
This paper presents a quick and very comprehensive tutorial on biclustering for the analysis of gene expression data obtained from microarray experiments. The results obtained from the conventional clustering methods to gene expression data are limited by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation also exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the gene expression matrix have been proposed to date. This simultaneous clustering, usually called as biclustering, which seeks to find submatrices, that is subgroups of genes and subgroups of columns, where the genes exhibit highly correlated activities for every condition. This type of algorithms has also been proposed and used in other fields, such as information retrieval and data mining. In this comprehensive tutorial, we analyze a number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search and the target applications.
Authors and Affiliations
Arabinda Panda , Satchidananda Dehuri
Keywords
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