Computational Discovery of Motifs Using Hierarchical Clustering Techniques
Discovery of motifs plays a key role in understanding gene regulation in organisms. Existing tools for motif discovery demonstrate some weaknesses in dealing with reliability and scalability. Therefore, development of advanced algorithms for resolving this problem will be useful. This paper aims to...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2008
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/11924/1/Computational%20Discovery_abstract.pdf http://ir.unimas.my/id/eprint/11924/ http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4781227 |
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| Summary: | Discovery of motifs plays a key role in understanding
gene regulation in organisms. Existing tools for motif discovery demonstrate some weaknesses in dealing with reliability and scalability. Therefore, development of advanced algorithms for resolving this problem will be useful. This paper aims to develop data mining techniques for discovering motifs. A mismatch based hierarchical clustering algorithm is proposed in this paper, where three heuristic rules for classifying clusters and a post-processing for ranking and refining the clusters are employed in the algorithm. Our algorithm is evaluated using two sets of DNA sequences with comparisons. Results demonstrate that the proposed techniques in this paper outperform MEME, AlignACE and SOMBRERO for most of the testing datasets. |
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