title: A two-stage exon recognition model based on synergetic neural network.
abstract: 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.
authors.affiliation: School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, China ; Cognitive Science Department, Xiamen University, Xiamen 361005, China.
authors.forename: Zhehuang
authors.initials: Z
authors.lastname: Huang
authors.affiliation: Cognitive Science Department, Xiamen University, Xiamen 361005, China ; Fujian Key Laboratory of the Brain-Like Intelligent Systems, Xiamen 361005, China.
authors.forename: Yidong
authors.initials: Y
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005091:Exons; D015894:Genome, Human; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D012984:Software