NDLI: Hybrid Artificial Bee Colony and Particle Swarm Optimization Approach to Protein Secondary Structure Prediction

Content Provider	IEEE Xplore Digital Library
Author	Mengwei Li Haibin Duan Dalong Shi
Copyright Year	2012
Description	Author affiliation: Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China (Mengwei Li) \|\| State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China (Haibin Duan; Dalong Shi)
Abstract	Proteins are crucial in the biological processes, and their structure determines whether they can function well or not. Since the theory presented by Anfinsen that proteins' space structure is entirely determined by the primary structure came out, it is possible for us to predict the structure of proteins through their primary structure without any experiment. In order to reach this target, the prediction problem can be formulated as an optimization problem that is set to find the lowest free energy conformation. In this paper, a hybrid Artificial Bee Colony (ABC) with Particle Swarm Optimization (PSO) Algorithm is used to solve this problem. Considering that the two algorithms have complementary characteristics, we combine them together and find out a better optimization results through this new approach. Experimental results have demonstrated the feasibility and effectiveness of our proposed approaches.
Starting Page	5040
Ending Page	5044
File Size	107451
Page Count	5
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6359433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Particle swarm optimization Amino acids Optimization Computational modeling Solid modeling Educational institutions Particle Swarm Optimization Protein structure prediction Artificial Bee Colony
Content Type	Text
Resource Type	Article

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