Title:	SISA: SEEDED ITERATIVE SIGNATURE ALGORITHM FOR BICLUSTERING GENE EXPRESSION DATA
Author(s):	Neelima Gupta , Seema Aggarwal
ISBN:	978-972-8924-63-8
Editors:	Hans Weghorn and Ajith P. Abraham
Year:	2008
Edition:	Single
Keywords:	Biclustering, Transcription Modules and Gene Expression Data.
Type:	Short Paper
First Page:	124
Last Page:	128
Language:	English
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Paper Abstract:	One approach to reduce the complexity of the task in the analysis of large scale genome-wide expression is to group the genes showing similar expression patterns into what are called transcription modules (TM). A TM is defined as a set of genes and a set of conditions under which these genes are most tightly co-expressed. There exist many algorithms for the analysis of gene expression data. Most of them compute non-overlapping TMs whereas a gene may be responsible for more than one cellular activity and hence must be included in more than one TMs. Existing algorithms like Signature Algorithm (SA) and Iterative Signature Algorithm (ISA) compute overlapping TMs. SA requires prior biological information of co-regulated genes which it takes as an input whereas ISA starts with a totally random input gene seed. Generating good seeds for ISA is a challenging problem. In this paper, we present an elegant way to generate an intelligent gene seed from the expression data itself. This eliminates the need to have prior information about coregulated genes. Experimental results were obtained for synthetic data as well as for the expression data for the yeast Saccharomyces cerevisiae. TMs obtained for the yeast data were found to be biologically and statistically significant using Gene Ontology database.
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