An enumerative biclustering algorithm based on greatest common divisor: application to DNA microarray data. (2017)

• Record Type: Journal Article
• Authors: Saber, Haifa Ben; Elloumi, Mourad
• Is Part Of: International journal of computer applications technology. Volume 55:Number 1(2017)
• Page Start: 55
• Page End: 60
• Publication Date: 2017
• Subjects: DNA microarray data -- biclustering -- greatest common divisor -- bioinformatics -- Galois lattice -- gene annotation; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

In a number of domains, such as DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called BestBinBicluster for biclustering of binary microarray data. Our biclustering algorithm BestBinBicluster extracts a group of biclusters from binary matrix Mb . This algorithm adopts the strategy of one bicluster at a time. It is a novel alternative to extract biclusters from binary data sets. Our algorithm is based on the use of a polynomial function to search Greatest Common Divisor (GCD) and the use of Galois Lattice. We propose to generate the Enumerative BinBicluster Lattice. The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data; our algorithm outperforms other biclustering algorithms for binary microarray data. We test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevant biclusters.