VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T06:34:35ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2018-04-18T08:49:12Z2018-04-18T08:49:12Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2948This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/29482018-04-18T08:49:12ZAn Efficient Ant Colony Optimization Algorithm for Protein Structure PredictionProtein structure prediction is considered as one of the most long-standing and challenging problem in bioinformatics. 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 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 efficiency of our algorithm in comparison with several state-of-the-art algorithms.Duc Dong Dodongdd@vnu.edu.vnPhuc Thai Dinhphuctd.95@gmail.comThi Ngoc Anh Vuanhvn2802@gmail.comLinh Trung Nguyenlinhtrung@vnu.edu.vn2017-12-29T08:50:38Z2018-01-04T14:27:21Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2892This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/28922017-12-29T08:50:38ZAnt colony optimization based founder sequence reconstructionReconstruction of founder (ancestor) genes for a given population is an important problem in evolutionary biology. It involves finding a set of genes that can combine together to form genes of all individuals in that population. Such reconstruction can be modeled as a combinatorial optimization problem, in which we have to find a set of founder (gene) sequences so that the individuals in a given population can be generated by the smallest number of recombination on these founder sequences. In this paper we propose a novel ant colony optimization algorithm (ACO) based method, equipped with some important improvements, for the founder gene sequence reconstruction problem. The proposed method yields excellent performance when validating on 108 test sets from three benchmark datasets. Comparing with the best by far method for founder sequence reconstruction, our proposed method performs better in 45 test sets, equally well in 44 and worse only in 19 sets. These experimental results demonstrate the efficacy and perspective of our proposed method.Anh Vu Thi NgocPhuc Thai DinhHoang Duc NguyenThanh Hai Danghai.dang@vnu.edu.vnDong Do Ducdongdd@vnu.edu.vn