Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction

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Mohammed Abo-Zahhad Abo-Zeid 1,* Sabah M. Ahmed 1 Shimaa A. Abd-Elrahman 1

1. Electrical and Electronics Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt

* Corresponding author.


Received: 15 Nov. 2013 / Revised: 3 Feb. 2014 / Accepted: 12 May 2014 / Published: 8 Jul. 2014

Index Terms

Artificial Neural Network, Exons, Introns, DNA Sequence, Circular Mapping, Donor Site, Acceptor Site


Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites). These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21). Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.

Cite This Paper

Mohammed Abo-Zahhad, Sabah M. Ahmed, Shimaa A. Abd-Elrahman, "Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.8, pp.51-57, 2014. DOI:10.5815/ijitcs.2014.08.07


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