Treffer: Application with a hybrid ant colony optimisation in motif detecting problem
Title:
Application with a hybrid ant colony optimisation in motif detecting problem
Authors:
Source:
International journal of computer applications in technology. 44(2):88-93
Publisher Information:
Genève: Inderscience Publishers, 2012.
Publication Year:
2012
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Bioinformatique, Bioinformatics, Bioinformática, Détection forme, Shape detection, Detección forma, Echantillonnage Gibbs, Gibbs sampling, Muestreo Gibbs, Efficacité, Efficiency, Eficacia, Intelligence en essaim, Swarm intelligence, Inteligencia de enjambre, Méthode échantillonnage, Sampling methods, Optimisation PSO, Particle swarm optimization, Optimización PSO, Révision, Revision, Revisión, Vie artificielle, Artificial life, Vida artificial, Gibbs sampling algorithm, ant colony optimisation, motif detection problem
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, Jilin Business and Technology College, Changchun 130062, China
Military Simulation Technology Institute, Air Force Aviation University, Changchun 130022, China
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao 028000, China
College of Mathematics, Jilin University, Changchun 130012, China
Military Simulation Technology Institute, Air Force Aviation University, Changchun 130022, China
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao 028000, China
College of Mathematics, Jilin University, Changchun 130012, China
ISSN:
0952-8091
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.26447083
Database:
PASCAL Archive
Weitere Informationen
In this paper, a hybrid optimisation algorithm for the motif detection problem of biological sequences is presented. Our method is improved Gibbs sampling method by employing an improved ant colony optimisation (ACO) algorithm. The goal of our method is to reduce the required computing time and get better solution. First, we find a set of better candidate positions for revising the motif by using an improved ACO. Then we use these candidate positions as the input to the Gibbs sampling method. The simulation results show that by employing our improved algorithm, both efficiency and quality for detecting motifs are improved compared with simple Gibbs sampling method.