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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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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my.unimas.ir.119242016-05-12T04:30:30Z http://ir.unimas.my/id/eprint/11924/ Computational Discovery of Motifs Using Hierarchical Clustering Techniques Wang, Dianhui Lee, Nung Kion QA75 Electronic computers. Computer science T Technology (General) 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. IEEE 2008 Conference or Workshop Item PeerReviewed text en http://ir.unimas.my/id/eprint/11924/1/Computational%20Discovery_abstract.pdf Wang, Dianhui and Lee, Nung Kion (2008) Computational Discovery of Motifs Using Hierarchical Clustering Techniques. In: 2008 Eighth IEEE International Conference on Data Mining, 15-19 Dec. 2008, PISA. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4781227 10.1109/ICDM.2008.21 |
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QA75 Electronic computers. Computer science T Technology (General) Wang, Dianhui Lee, Nung Kion Computational Discovery of Motifs Using Hierarchical Clustering Techniques |
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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. |
format |
Conference or Workshop Item |
author |
Wang, Dianhui Lee, Nung Kion |
author_facet |
Wang, Dianhui Lee, Nung Kion |
author_sort |
Wang, Dianhui |
title |
Computational Discovery of Motifs Using Hierarchical Clustering Techniques |
title_short |
Computational Discovery of Motifs Using Hierarchical Clustering Techniques |
title_full |
Computational Discovery of Motifs Using Hierarchical Clustering Techniques |
title_fullStr |
Computational Discovery of Motifs Using Hierarchical Clustering Techniques |
title_full_unstemmed |
Computational Discovery of Motifs Using Hierarchical Clustering Techniques |
title_sort |
computational discovery of motifs using hierarchical clustering techniques |
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IEEE |
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2008 |
url |
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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1644511303854718976 |
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13.18916 |