Paper Info
Title
A Novel Particle Swarm-Based Approach for 3D Motif Matching and Protein Structure Classification.
Abstract
This paper investigates the applicability of Particle Swarm Optimization (PSO) to motif matching in protein structures, which can help in protein structure classification and function annotation. A 3D motif is a spatial, local pattern in a protein structure important for its function. In this study, the problem of 3D motif matching is formulated as an optimization task with an objective function of minimizing the least Root Mean Square Deviation (lRMSD) between the query motif and target structures. Evaluation results on two protein datasets demonstrate the ability of the proposed approach on locating the true query motif of all 66 target proteins almost always (9 and 8 times, respectively, on average, out of 10 trials per target). A large-scale application of motif matching is protein classification, where the proposed approach distinguished between the positive and negative examples by consistently ranking all positive examples at the very top of the search results.
Year
DOI
Venue
2014
10.1007/978-3-319-06483-3_1
ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2014
Keywords
Field
DocType
Particle Swarm Optimization (PSO),3D Motif Matching,Protein Structure Classification,least Root Mean Square Deviation (lRMSD)
Particle swarm optimization,Annotation,Ranking,Pattern recognition,Computer science,Motif (music),Root-mean-square deviation,Artificial intelligence,Almost surely,Machine learning,Protein structure
Conference
Volume
ISSN
Citations 
8436
0302-9743
1
PageRank 
References 
Authors
0.35
8
2
Name
Order
Citations
PageRank
Hazem Radwan Ahmed131.40
Janice I. Glasgow2392127.97