Hash based biclustering for class discovery from gene expression data: A pattern similarity approach

Cellular processes come forth on subsets of genes to be co-expressed and correlated under certain experimental conditions, but behaves almost independently under other conditions. So, discovering such local expression patterns may uncover many genetic mechanisms which lead to class discovery. In this paper, we have proposed an efficient hash based biclustering approach, which identifies coherent patterns known as scaling and shifting patterns from high dimensional gene expression datasets. Our proposed algorithm consists of two steps: first, we pre-process the data set by reducing the attributes without much loss of information using Principal Component Analysis (PCA) and second, an enhanced pCluster algorithm using hashing technique to make the searching faster is used to discover the scaling and shifting patterns, which leads to patter based biclusters and those biclusters will contribute for class discovery. Finally, we have compared our method with some existing pattern based models and it has been found that our algorithm is very versatile and promising.