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ISSN: 2168-9679

Journal of Applied & Computational Mathematics
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Research Article

A Novel Artificial Neural Network and an Improved Particle Swarm Optimization used in Splice Site Prediction

Wei Bin1* and Zhao Jing2,3

1Engineering University of CAPF, Xi’an 710086, China

2Xijing University, Xi’an 710123, China

3Xi’an Jiaotong University, Xi’an 710049, China

*Corresponding Author:
Wei Bin
Engineering University of CAPF
Xi’an 710086, China
Tel: 86-13991387365
E-mail: [email protected]

Received Date: April 21, 2014; Accepted Date: June 11, 2014; Published Date: June 16, 2014

Citation: Bin W, Jing Z (2014) A Novel Artificial Neural Network and an Improved Particle Swarm Optimization used in Splice Site Prediction. J Appl Computat Math 3:166. doi: 10.4172/2168-9679.1000166

Copyright: © 2014 Bin W, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The amount of DNA sequence data produced by several genomic projects had increased dramatically in recent years. One of the main goals of bioinformatics was to identify genes. A crucial part of the gene identification was to precisely detect the exon intron boundaries, i.e. the splice sites. This paper introduced a new type of artificial neural network (called NANN), which was designed specifically to solve the splice sites prediction problem. Moreover, the network connection weights of NANN were determined by an improved particle swarm optimization which was inspired by the wolves' activities circle. In addition, three types of encoding approaches were applied to generate the input for the NANN. Intensive experiments were presented in this paper, and the results showed that our algorithm was better than some current methods, that is, the NANN_IPSO was applicable to splice site prediction problem.

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