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Splice Site Detection in DNA Sequences using Probabilistic Neural Network

by Tripti Nassa, Shailendra Singh, Neelam Goel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 4
Year of Publication: 2013
Authors: Tripti Nassa, Shailendra Singh, Neelam Goel
10.5120/13232-0664

Tripti Nassa, Shailendra Singh, Neelam Goel . Splice Site Detection in DNA Sequences using Probabilistic Neural Network. International Journal of Computer Applications. 76, 4 ( August 2013), 1-4. DOI=10.5120/13232-0664

@article{ 10.5120/13232-0664,
author = { Tripti Nassa, Shailendra Singh, Neelam Goel },
title = { Splice Site Detection in DNA Sequences using Probabilistic Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 4 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number4/13232-0664/ },
doi = { 10.5120/13232-0664 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:59.657965+05:30
%A Tripti Nassa
%A Shailendra Singh
%A Neelam Goel
%T Splice Site Detection in DNA Sequences using Probabilistic Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 4
%P 1-4
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With numerous of genomes sequenced, gene prediction has become a challenging problem in bioinformatics. Gene prediction helps in identifying physical and mental features of different organisms. A large number of gene prediction tools have been developed in the past two decades. Splice site detection method lies at the heart of ab-initio gene prediction tools and plays an important role in detecting the exon boundaries. In this paper, a method for detecting splice sites by using generalized regression neural network is proposed. The proposed method uses conditional probabilities to preprocess the input which enables it to incorporate the already known sequence features from biological knowledge. The experimental results show that the application of this new architecture to splice site detection has greatly improved the training time and reduces the false positive predictions.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Gene prediction acceptor sites donor sites neural networks conditional probability consensus sequence

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