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The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shiftingand- scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named SSSim (Shifting and Scaling Similarity), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named ICS (Intensive Correlation Search) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression dataset. The technique performs satisfactorily with a number of benchmarked gene expression datasets when evaluated in terms of functional categories in Gene Ontology database.
Jugal Kalita, "Shifting-and-scaling Correlation based Biclustering Algorithm", IEEE/ACM Transactions on Computational Biology and Bioinformatics, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TCBB.2014.2323054
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