Discovering Consensus Patterns in Biological Databases

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Author

MY ElTabakh, WG Aref, M Ouzzani, MH Ali

Entry type

inbook

Abstract

Consensus patterns, like motifs and tandem repeats, are highly conserved patterns with very few substitutions where no gaps are allowed. In this paper, we present a progressive hierarchical clustering technique for discovering consensus patterns in biological databases over a certain length range. This technique can discover consensus patterns with various requirements by applying a post-processing phase. The progressive nature of the hierarchical clustering algorithm makes it scalable and efficient. Experiments to discover motifs and tandem repeats on real biological databases show significant performance gain over non-progressive clustering techniques.

Date

2006

Booktitle

Data Mining and Bioinformatics

Key alpha

Aref

Pages

170-184

Publisher

Springer Berlin / Heidelberg

Series

Lecture Notes in Computer Science

Volume

4316

Publication Date

2006-00-00

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@Inbook{ Aref,
	title = "Discovering Consensus Patterns in Biological Databases",
	author = "MY ElTabakh, WG Aref, M Ouzzani, MH Ali",
	year = "2006",
	booktitle = "Data Mining and Bioinformatics",
	pages = "170-184",
	publisher = "Springer Berlin / Heidelberg",
	series = "Lecture Notes in Computer Science",
	volume = "4316",
	abstract = "Consensus patterns, like motifs and tandem repeats, are highly conserved patterns with very few substitutions where no gaps are allowed. In this paper, we present a progressive hierarchical clustering technique for discovering consensus patterns in biological databases over a certain length range. This technique can discover consensus patterns with various requirements by applying a post-processing phase. The progressive nature of the hierarchical clustering algorithm makes it scalable and efficient. Experiments to discover motifs and tandem repeats on real biological databases show significant performance gain over non-progressive clustering techniques.",
}