Discovering consensus patterns in biological databases

被引：0
|
作者：
ElTabakh, Mohamed Y. [1 ]
Aref, Walid G. [1 ]
Ouzzani, Mourad [2 ]
Ali, Mohamed H. [1 ]
机构：
[1] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47906 USA
[2] Purdue Univ, Cyber Ctr, W Lafayette, IN 47906 USA
来源：
DATA MINING AND BIOINFORMATICS | 2006年 / 4316卷
关键词：
D O I：
暂无
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
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.
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收藏
页码：170 / +
页数：3
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