eprintid: 2894
rev_number: 19
eprint_status: archive
userid: 290
dir: disk0/00/00/28/94
datestamp: 2017-12-29 08:56:20
lastmod: 2018-01-10 08:59:42
status_changed: 2018-01-10 08:59:42
type: monograph
metadata_visibility: show
creators_name: Do, Duc Dong
creators_id: dongdd@vnu.edu.vn
title: A Novel and Efficient Ant Colony Optimization Algorithm for Protein 3D Structure Prediction
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: Protein structure prediction (PSP) is considered as one of the most long-standing and challenging problem in bioinformatic. In this paper, we present an efficient ant
colony optimization algorithm to predict the protein structure on three-dimensional face-centered cubic lattice coordinates, using hydrophobic-polar (HP) model and MiyazawaJernigan (MJ) model to calculate the free energy. The reinforcement learning information is expressed in the k-order Markov model, the heuristic information is determined based on the increase of the
total energy. On a set of benchmark proteins, the results show a remarkable efficiency of our algorithm by comparing with the state-of-the-art algorithms.
date: 2017
date_type: published
publisher: VNU-UET Technical Report
id_number: TR2017-FIT-002
full_text_status: public
monograph_type: technical_report
pages: 7
citation:   Do, Duc Dong  (2017) A Novel and Efficient Ant Colony Optimization Algorithm for Protein 3D Structure Prediction.  Technical Report. VNU-UET Technical Report.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2894/1/TR2017-FIT-002.pdf