Mining gene expression datasets using density-based clustering

Authors:
Seokkyung Chung;Jongeun Jun;Dennis McLeod
Affiliations:
University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA
Venue:
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Year:
2004

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Abstract

Given the recent advancement of microarray technologies, we present a density-based clustering approach for the purpose of co-expressed gene cluster identification. The underlying hypothesis is that a set of co-expressed gene clusters can be used to reveal a common biological function. By addressing the strengths and limitations of previous density-based clustering approaches, we present a novel clustering algorithm that utilizes a neighborhood defined by k-nearest neighbors. Experimental results indicate that the proposed method identifies biologically meaningful and co-expressed gene clusters.