title: Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides.
abstract: The present paper is a novel contribution to the field of bioinformatics by using grammatical inference in the analysis of data. We developed an algorithm for generating star-free regular expressions which turned out to be good recommendation tools, as they are characterized by a relatively high correlation coefficient between the observed and predicted binary classifications. The experiments have been performed for three datasets of amyloidogenic hexapeptides, and our results are compared with those obtained using the graph approaches, the current state-of-the-art methods in heuristic automata induction, and the support vector machine. The results showed the superior performance of the new grammatical inference algorithm on fixed-length amyloid datasets.
authors.affiliation: Faculty of Computer Science and Materials Science, University of Silesia, Ulica Zytnia 12, 41-200 Sosnowiec, Poland.
authors.forename: Wojciech
authors.identifier: 0000-0003-3191-9151
authors.initials: W
authors.lastname: Wieczorek
authors.affiliation: Department of Computer Engineering, Faculty of Electronics, Wroclaw University of Science and Technology, Wybrzeże Wyspianskiego 27, 50-370 Wroclaw, Poland.
authors.forename: Olgierd
authors.identifier: 0000-0003-4722-176X
authors.initials: O
authors.lastname: Unold
mesh_terms: D000465:Algorithms; D000682:Amyloid / Q000737:chemistry*; D001331:Automation; D019295:Computational Biology; D016208:Databases, Factual; D005189:False Positive Reactions; D006801:Humans; D007802:Language; D008954:Models, Biological; D015233:Models, Statistical; D010455:Peptides / Q000737:chemistry*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D000682:Amyloid; D010455:Peptides