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Recurrent Neural Network based Classification of Protein-Protein Interactions

by Dilpreet Kaur, Shailendra Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 4
Year of Publication: 2012
Authors: Dilpreet Kaur, Shailendra Singh
10.5120/8188-1549

Dilpreet Kaur, Shailendra Singh . Recurrent Neural Network based Classification of Protein-Protein Interactions. International Journal of Computer Applications. 52, 4 ( August 2012), 6-11. DOI=10.5120/8188-1549

@article{ 10.5120/8188-1549,
author = { Dilpreet Kaur, Shailendra Singh },
title = { Recurrent Neural Network based Classification of Protein-Protein Interactions },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 4 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number4/8188-1549/ },
doi = { 10.5120/8188-1549 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:24.014066+05:30
%A Dilpreet Kaur
%A Shailendra Singh
%T Recurrent Neural Network based Classification of Protein-Protein Interactions
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 4
%P 6-11
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Proteomics is an attempt to describe or explain biological state and qualitative and quantitative changes of protein content of cells and extracellular biological materials under different conditions to further understand biological processes. Protein-Protein interaction prediction and classification is a very important task. Prediction and classification of protein-protein interactions can help in improving the understanding of diseases and can provide the basis for new therapeutic approaches. In this work a model is proposed to classify protein-protein interactions. Jordan Recurrent Neural Network is used to classify the protein-protein interactions. The model developed gives 97. 25% of accuracy which is 8. 7% more than Back-Propagation Neural Network.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Protein-Protein Interactions Jordan Recurrent Neural Network Back-Propagation (BP) Neural Network SVM SVM-KNN Amino Acid Composition

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