Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37752
Title: An effective data mining technique for multi-class protein sequence classification
Authors: Ma, PCH
Chan, KCC 
Keywords: Biology computing
Cellular biophysics
Data mining
Molecular biophysics
Molecular configurations
Pattern classification
Proteins
Sequences
Issue Date: 2008
Source: Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2008), Shanghai, China, May 16-18, 2008, p. 486-489 How to cite?
Abstract: One way to understand the molecular mechanism of a cell is to understand the function of each protein encoded in its genome. The function of a protein is largely dependent on the three-dimensional structure the protein assumes after folding. Since the determination of three-dimensional structure experimentally is difficult and expensive, an easier and cheaper approach is for one to look at the primary sequence of a protein and to determine its function by classifying the sequence into the corresponding functional family. In this paper, we propose an effective data mining technique for the multi-class protein sequence classification. For experimentations, the proposed technique has been tested with different sets of protein sequences. Experimental results show that it outperforms other existing protein sequence classifiers and can effectively classify proteins into their corresponding functional families.
URI: http://hdl.handle.net/10397/37752
ISBN: 978-1-4244-1747-6
978-1-4244-1748-3 (E-ISBN)
Appears in Collections:Conference Paper

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