A Co-clustering Technique for Gene Expression Data using Bi-partite Graph Approach
Mining microarray data sets is vital in bioinformatics research and medical applications. There has been extensive research on co-clustering of gene expression data generated using cDNA microarrays. Co-clustering approach is an important analysis tool in gene expression measurement, when some genes have multiple functions and experimental conditions are diverse. In this paper, we introduce a new framework for microarray gene expression data co-clustering. The basis of this framework is a bipartite graph representation of 2-dimensional gene expression data. We have constructed this bipartite graph by partitioning the sample set into two disjoint sets. The key property of this representation is that, for a gene×sample matrix, it constructs the range bipartite graph, a compact representation of all similar value ranges between sample columns. In order to produce the set of co-clusters, it searches for constrained maximal cliques in this bipartite graph. Our method is scalable to practical gene expression data and can find some interesting co-clusters in real microarray datasets that meet specific input conditions.
Suvendu Kanungo Gadadhar Sahoo Manoj Madhava Gore
Dept.of Comp.Sc.BIT Mesra, Ranchi Allhabad Campus, UP, India Dept.of IT & MCA BIT Mesra, Ranchi Jharkhand, India Dept.of Comp.Sc.& Engg.MNNIT Allahabad, UP, India
国际会议
成都
英文
1-5
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）