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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 503132, 7 pages
Research Article

A Two-Stage Exon Recognition Model Based on Synergetic Neural Network

1School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, China
2Cognitive Science Department, Xiamen University, Xiamen 361005, China
3Fujian Key Laboratory of the Brain-Like Intelligent Systems, Xiamen 361005, China

Received 30 January 2014; Revised 27 February 2014; Accepted 3 March 2014; Published 25 March 2014

Academic Editor: Xiao-Qin Xia

Copyright © 2014 Zhehuang Huang and Yidong Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.