FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information
The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Penerbit Universiti Kebangsaan Malaysia
2012
|
Online Access: | http://journalarticle.ukm.my/5920/1/jqma-8-2-makalah2.pdf http://journalarticle.ukm.my/5920/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my-ukm.journal.5920 |
---|---|
record_format |
eprints |
spelling |
my-ukm.journal.59202016-12-14T06:39:52Z http://journalarticle.ukm.my/5920/ FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information Sakhinah Abu Bakar, Javid Taheri, Albert Y Zomaya, The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods. Penerbit Universiti Kebangsaan Malaysia 2012-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/5920/1/jqma-8-2-makalah2.pdf Sakhinah Abu Bakar, and Javid Taheri, and Albert Y Zomaya, (2012) FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information. Journal of Quality Measurement and Analysis, 8 (2). pp. 9-20. ISSN 1823-5670 :http://pkukmweb.ukm.my/ppsmfst/jqma/index2.html |
institution |
Universiti Kebangsaan Malaysia |
building |
Perpustakaan Tun Sri Lanang Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Kebangsaan Malaysia |
content_source |
UKM Journal Article Repository |
url_provider |
http://journalarticle.ukm.my/ |
language |
English |
description |
The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods. |
format |
Article |
author |
Sakhinah Abu Bakar, Javid Taheri, Albert Y Zomaya, |
spellingShingle |
Sakhinah Abu Bakar, Javid Taheri, Albert Y Zomaya, FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information |
author_facet |
Sakhinah Abu Bakar, Javid Taheri, Albert Y Zomaya, |
author_sort |
Sakhinah Abu Bakar, |
title |
FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information |
title_short |
FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information |
title_full |
FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information |
title_fullStr |
FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information |
title_full_unstemmed |
FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information |
title_sort |
fis-pnn: a hybrid computational method for protein-protein interactions prediction using the secondary structure information |
publisher |
Penerbit Universiti Kebangsaan Malaysia |
publishDate |
2012 |
url |
http://journalarticle.ukm.my/5920/1/jqma-8-2-makalah2.pdf http://journalarticle.ukm.my/5920/ |
_version_ |
1643736589899137024 |
score |
13.18916 |