title: Protein phylogenetic inference using maximum likelihood with a genetic algorithm.
abstract: This paper presents a method to construct phylogenetic trees from amino acid sequences. Our method uses a maximum likelihood approach which gives a confidence score for each possible alternative tree. Based on this approach, we developed a genetic algorithm for exploring the best score tree: randomly generated alternative trees are rearranged so that their scores are improved by utilizing crossover and mutation operators. In a test of our algorithm on a data set of EF-1 alpha sequences, we found that the performance of our algorithm is comparable to that of other tree-construction methods.
authors.affiliation: Department of Information and Computer Sciences, Faculty of Engineering Science, Osaka University, Japan.
authors.forename: H
authors.initials: H
authors.lastname: Matsuda
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D003198:Computer Simulation; D016208:Databases, Factual; D019143:Evolution, Molecular; D008957:Models, Genetic; D020648:Peptide Elongation Factor 1; D010445:Peptide Elongation Factors / Q000737:chemistry / Q000235:genetics; D010802:Phylogeny; D011336:Probability; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D012984:Software
chemical_list: D020648:Peptide Elongation Factor 1; D010445:Peptide Elongation Factors; D011506:Proteins