TY  - CONF
ID  - SisLab2948
UR  - http://www.ismict2018.org
A1  - Do, Duc Dong
A1  - Thai Dinh, Phuc
A1  - Vu, Thi Ngoc Anh
A1  - Nguyen, Linh Trung
Y1  - 2018/03/26/
N2  - Protein structure prediction is considered as one of the most long-standing and challenging problem in bioinformatics. In this paper, we present an ef?cient ant colony optimization algorithm to predict the protein structure on three-dimensional face-centered cubic lattice coordinates, using the hydrophobic?polar model and the Miyazawa-Jernigan model to calculate the free energy. The reinforcement learning information is expressed in the k-order Markov model, and the heuristic information is determined based on the increase of the total energy. On a set of benchmark proteins, the results show a remarkable ef?ciency of our algorithm in comparison with several state-of-the-art algorithms.
TI  - An Ef?cient Ant Colony Optimization Algorithm for Protein Structure Prediction
SP  - 28
M2  - Sydney, Australia
AV  - public
EP  - 33
T2  - 12th International Symposium on Medical Information and Communication Technology (ISMICT)
ER  -