Discovering Consensus Patterns in Biological Databases
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. T...
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| creator | ElTabakh, Mohamed Y. Aref, Walid G. Ouzzani, Mourad Ali, Mohamed H. |
| description | 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. |
| doi_str_mv | 10.1007/11960669_15 |
| format | Conference Proceeding |
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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. 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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.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11960669_15</doi><tpages>15</tpages></addata></record> |
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| title | Discovering Consensus Patterns in Biological Databases |
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