A Two-Stage Exon Recognition Model Based on Synergetic Neural Network
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-25
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Huang, Zhehuang& Chen, Yidong. 2014. A Two-Stage Exon Recognition Model Based on Synergetic Neural Network. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-476761
Modern Language Association (MLA)
Huang, Zhehuang& Chen, Yidong. A Two-Stage Exon Recognition Model Based on Synergetic Neural Network. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-476761
American Medical Association (AMA)
Huang, Zhehuang& Chen, Yidong. A Two-Stage Exon Recognition Model Based on Synergetic Neural Network. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-476761
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-476761