On clustering of genes

Authors:
Raja Loganantharaj;Satish Cheepala;John Clifford
Affiliations:
Bioinformatics Research Lab, University of Louisiana at Lafayette, Lafayette, LA;Department of Biochemistry and Molecular Biology, LSU Health Sciences Center and Feist-Weiller Cancer Center, Shreveport, LA;Department of Biochemistry and Molecular Biology, LSU Health Sciences Center and Feist-Weiller Cancer Center, Shreveport, LA
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

Citing 2
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An integrated tool for microarray data clustering and cluster validity assessment

Proceedings of the 2004 ACM symposium on Applied computing
An interactive power analysis tool for microarray hypothesis testing and generation

Bioinformatics

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Abstract

The availability of microarray technology at an affordable price makes it possible to determine expression of several thousand genes simultaneously. Gene expression can be clustered so as to infer the regulatory modules and functionality of a gene relative to one or more of the annotated genes of the same cluster. The outcome of clustering depends on the clustering method and the metric being used to measure the distance. In this paper we study the popular hierarchal clustering algorithm and verify how many of the genes in the same cluster share functionality. Further, we will also look into the supervised clustering method for satisfying hypotheses and view how many of these genes are functionally related.