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...

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Main Authors:	Sakhinah Abu Bakar, Javid Taheri, Albert Y Zomaya
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2012
Online Access:	http://journalarticle.ukm.my/5920/ http://journalarticle.ukm.my/5920/ http://journalarticle.ukm.my/5920/1/jqma-8-2-makalah2.pdf

id	ukm-5920
recordtype	eprints
spelling	ukm-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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
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/ http://journalarticle.ukm.my/5920/ http://journalarticle.ukm.my/5920/1/jqma-8-2-makalah2.pdf
first_indexed	2023-09-18T19:45:31Z
last_indexed	2023-09-18T19:45:31Z
_version_	1777405873510940672

FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information

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