Briefs

A new approach to identifying patterns in gene expression analysis has been shown to be more effective than the most popular method in a joint Penn State and University at Buffalo study. Using two published gene expression data sets as test cases, the research team found that the KL clustering method, which uses a novel measure of similarity (Kullback-Leibler) not previously used for gene expression analysis, was superior to the most popular method, hierarchical clustering, in separating the data into dense clusters with similar patterns.

In gene expression analysis, the identification of groups of genes with similar temporal patterns of expression is usually a critical step because it provides insights into gene-gene interactions and the underlying biological processes. Experiments suggest that genes with similar function may exhibit similar temporal patterns of co-regulation.

Dr. Raj Acharya, professor and head of the Department of Computer Science and Engineering at Penn State, says that, although the study was conducted with gene data, KL clustering could be applied to any large set of temporal data.

The team published their findings in a paper, "An information theoretic approach for analyzing temporal patterns of gene expression," in the March issue of the journal, Bioinformatics. The authors are Jyotsna Kasturi, Penn State doctoral candidate, Acharya, and Dr. Murali Ramanathan, Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York.

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