Protein secondary structures prediction using data fusion approach

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Title:	Protein secondary structures prediction using data fusion approach
Authors:	Chu, Yen-Wei;Yu, Chin-Sheng;Ng, Hui-Fuang
Contributors:	Department of Bioinformatics
Keywords:	Forecasting;Fusion reactions;Industrial engineering;Information fusion;Intelligent control;Nuclear physics;Stages;Biological features;Clustering;Control and automation;Data mining;Experimental analysis;Fusion mechanism;Genetic algorithms;Knowledge discovery;New approaches;Predictive information;Protein Classification;Protein secondary structure;Protein secondary structures;Protein secondary-structure prediction;Protein structures;Real structures;Schemas;Two stages
Date:	2008
Issue Date:	2010-04-08 12:06:08 (UTC+0)
Publisher:	Asia University
Abstract:	The importance of secondary protein structures is to help us to recognize many biological features, such as the structure and function of a protein, the evolutionary relation between proteins, and protein classification. Unfortunately, the secondary protein structures are hard to get from experimental analysis, and most researchers usually use predictive information instead of real structures. For protein secondary structure prediction, this research takes the predictive results from PSIPRED and PROF as the profile into the two-stage data fusion mechanism. The successive stage will integrate first stage outputs with our schemas. By performing the new approach, the accuracy of Q3 can be improved 5% more than the worst methods (PSIPRED or PROF) in the RS126 and CB513. © 2008 IEEE.
Relation:	Proceedings of the World Congress on Intelligent Control and Automation (WCICA) :1885-1890
Appears in Collections:	[生物資訊與醫學工程學系 ] 會議論文

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