Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23549
Title: A structural data mining approach for the classification of secondary RNA structure
Authors: Lam, WWM
Chan, KCC 
Keywords: Biology computing
Data mining
Genetics
Graphs
Macromolecules
Molecular biophysics
Molecular configurations
Issue Date: 2006
Publisher: IEEE
Source: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005 : IEEE-EMBS 2005, 17-18 January 2006, Shanghai, p. 4759-4762 How to cite?
Abstract: There exist many methods for classifying genomic data by aligning, comparing, and analyzing primary nucleotide sequences using such algorithms as dynamic programming and kinetic folding, etc.. These methods are, however, not always effective as motifs are more conserved in structures than in sequences. Instead of performing classification based on primary sequences, we therefore propose to perform the task from structure, exploiting the phenomenon in which molecules form from a sequence of nucleotides, beginning with a primary sequence that can fold back onto itself to form a secondary structure and then a tertiary structure. The algorithm we propose is able to perform data mining in structural data and is called the random multi-level attributed (RMLA) graph algorithm for mining and representing secondary genomic structure from such biomolecule as tRNA. The identification of structural similarity is implemented with information measure concept to characterize the resultant class. Experiments are based on known tRNA structural data from database of compilation of tRNA genes. The results show that our approach is able to effectively classify different class of tRNA secondary structure. We also compare our result with other classification algorithms to prove the effectiveness. Our approach shows a better way to classify structural data. In fact, RMLA graph is not suitable only for the classification of genomic data, wherever graphs are used to model data, it is useful for discovering patterns in the databases
URI: http://hdl.handle.net/10397/23549
ISBN: 0-7803-8741-4
DOI: 10.1109/IEMBS.2005.1615535
Appears in Collections:Conference Paper

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