Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network

Ant colony optimization (ACO) is a metaheuristic algorithm that has been successfully applied to several types of optimization problems such as scheduling, routing, and more recently for solving protein functional module detection (PFMD) problem in protein-protein interaction (PPI) networks. For a s...

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Main Author: Sallim, Jamaludin
Format: Thesis
Language:en
Published: 2017
Subjects:
Online Access:http://eprints.usm.my/45393/1/JAMALUDIN%20SALLIM.pdf
http://eprints.usm.my/45393/
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author Sallim, Jamaludin
author_facet Sallim, Jamaludin
author_sort Sallim, Jamaludin
building Hamzah Sendut Library
collection Institutional Repository
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
continent Asia
country Malaysia
description Ant colony optimization (ACO) is a metaheuristic algorithm that has been successfully applied to several types of optimization problems such as scheduling, routing, and more recently for solving protein functional module detection (PFMD) problem in protein-protein interaction (PPI) networks. For a small PPI data size, ACO has been successfully applied to but it is not suitable for large and noisy PPI data, which has caused to premature convergence and stagnation in the searching process. To cope with the aforementioned limitations, we propose two new enhancements of ACO to solve PFMD problem.
format Thesis
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institution Universiti Sains Malaysia
language en
publishDate 2017
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spelling my.usm.eprints.45393 http://eprints.usm.my/45393/ Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network Sallim, Jamaludin QA75.5-76.95 Electronic computers. Computer science Ant colony optimization (ACO) is a metaheuristic algorithm that has been successfully applied to several types of optimization problems such as scheduling, routing, and more recently for solving protein functional module detection (PFMD) problem in protein-protein interaction (PPI) networks. For a small PPI data size, ACO has been successfully applied to but it is not suitable for large and noisy PPI data, which has caused to premature convergence and stagnation in the searching process. To cope with the aforementioned limitations, we propose two new enhancements of ACO to solve PFMD problem. 2017-07 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/45393/1/JAMALUDIN%20SALLIM.pdf Sallim, Jamaludin (2017) Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Sallim, Jamaludin
Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_full Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_fullStr Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_full_unstemmed Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_short Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_sort heuristic-based ant colony optimization algorithm for protein functional module detection in protein interaction network
topic QA75.5-76.95 Electronic computers. Computer science
url http://eprints.usm.my/45393/1/JAMALUDIN%20SALLIM.pdf
http://eprints.usm.my/45393/
url_provider http://eprints.usm.my/