Issue No. 06 - Nov.-Dec. (2014 vol. 11)
Hasin Afzal Ahmed , Department of Computer Science and Engineering, Tezpur University, Assam, India
Priyakshi Mahanta , Department of Computer Science and Engineering, Tezpur University, Assam, India
Dhruba Kumar Bhattacharyya , Department of Computer Science and Engineering, Tezpur University, Assam, India
Jugal Kumar Kalita , Department of Computer Science, University of Colorado, Colorado Springs, CO
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 shifting-and-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 Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in Gene Ontology database.
Correlation, Gene expression, Noise measurement, Noise, Clustering algorithms, Algorithm design and analysis,shifting-and-scaling correlation, Similarity measure, biclustering, gene expression data analysis
Hasin Afzal Ahmed, Priyakshi Mahanta, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita, "Shifting-and-Scaling Correlation Based Biclustering Algorithm", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. , pp. 1239-1252, Nov.-Dec. 2014, doi:10.1109/TCBB.2014.2323054