Computational Discovery of Motifs Using Hierarchical Clustering Techniques

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.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Computational, discovering motifs, hierarchical clustering techniques, DNA, genes, mismatch based hierarchical clustering, algorithm, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Karen Kornalius
Date Deposited: 12 May 2016 04:30
Last Modified: 12 May 2016 04:30
URI: http://ir.unimas.my/id/eprint/11924

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