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. 2014:2014:503132.
doi: 10.1155/2014/503132. Epub 2014 Mar 25.

A two-stage exon recognition model based on synergetic neural network

Zhehuang Huang  1 , Yidong Chen  2
Affiliations

A two-stage exon recognition model based on synergetic neural network

Zhehuang Huang et al. Comput Math Methods Med. 2014.

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.

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Figures

Figure 1
Figure 1
Structure of eukaryotic DNA sequence.
Figure 2
Figure 2
Exon recognition algorithm based on 3-Cycle spectrum.
Figure 3
Figure 3
The power spectrum of viral gene sequence.
Figure 4
Figure 4
Exon recognition based on synergetic neural network.
Figure 5
Figure 5
The SNR distribution of 200 mammalian exons.
Figure 6
Figure 6
The SNR distribution of 200 mammalian introns.
Algorithm 1
Algorithm 1
Determination of initial exon region based on synergetic neural network.
Algorithm 2
Algorithm 2
Precise exon regions based on adaptive smoothing window.
Algorithm 3
Algorithm 3
Parameter optimization based on artificial fish swarm algorithm.
See this image and copyright information in PMC

References

    1. Burge CB, Karlin S. Finding the genes in genomic DNA. Current Opinion in Structural Biology. 1998;8(3):346–354. - PubMed
    1. Wang Z, Chen Y, Li Y. A brief review of computational gene prediction methods. Genomics Proteomics Bioinformatics. 2004;2(4):216–221. - PMC - PubMed
    1. Sharma SD, Shakya K, Sharma SN. Evaluation of DNA mapping schemes for exon detection. Proceedings of the International Conference on Computer, Communication and Electrical Technology (ICCCET '11); March 2011; pp. 71–74.
    1. Kotlar D, Lavner Y. Gene prediction by spectral rotation measure: a new method for identifying protein-coding regions. Genome Research. 2003;13(8):1930–1937. - PMC - PubMed
    1. Yin C, Yau SS-T. Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence. Journal of Theoretical Biology. 2007;247(4):687–694. - PubMed

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