Mining gene expression datasets using density-based clustering

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Mining gene expression datasets using density-based clustering

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Conference on Information and Knowledge Management archive
Proceedings of the thirteenth ACM international conference on Information and knowledge management table of contents

Washington, D.C., USA

POSTER SESSION: Posters P-1 table of contents

Pages: 150 - 151

Year of Publication: 2004

ISBN:1-58113-874-1

Authors

Seokkyung Chung	University of Southern California, Los Angeles, CA
Jongeun Jun	University of Southern California, Los Angeles, CA
Dennis McLeod	University of Southern California, Los Angeles, CA

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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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 <i>k</i>-nearest neighbors. Experimental results indicate that the proposed method identifies biologically meaningful and co-expressed gene clusters.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	S. Chung, J. Jun, and D. McLeod. Mining gene expression datasets using density-based clustering. In USC/IMSC Technical Report, IMSC-04-002, 2004.
	2	S. Chung, and D. McLeod. Dynamic topic mining from news stream data. In Proceedings of ODBASE, 2003.
	3	P. T. Spellman et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces Cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9(12):3273--3297, 1998.