文章基本信息

标题：IMPROVED BICLUSTERING ALGORITHM FOR GENE EXPRESSION DATA
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作者：SADIQ HUSSAIN ; PROF. G.C. HAZARIKA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2011
卷号：32
期号：1
页码：08-14
出版社：Journal of Theoretical and Applied
摘要：Biclustering algorithms simultaneously cluster both rows and columns. These types of algorithms are applied to gene expression data analysis to find a subset of genes that exhibit similar expression pattern under a subset of conditions. Cheng and Church introduced the mean squared residue measure to capture the coherence of a subset of genes over a subset of conditions. They provided a set of heuristic algorithms based primarily on node deletion to find one bicluster or a set of biclusters after masking discovered biclusters with random values. The mean squared residue is a popular measure of bicluster quality. One drawback however is that it is biased toward flat biclusters with low row variance. In this paper, we introduce an improved bicluster score that removes this bias and promotes the discovery the most significant biclusters in the dataset. We employ this score within a new biclustering approach based on the bottom up search strategy. We believe that the bottom-up search approach better models the underlying functional modules of the gene expression dataset.
关键词：Bicluster; Gene expression Data; Clustering; Pattern Recognition