Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides
Joint Authors
Wieczorek, Wojciech
Unold, Olgierd
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-09
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wieczorek, Wojciech& Unold, Olgierd. 2016. Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100068
Modern Language Association (MLA)
Wieczorek, Wojciech& Unold, Olgierd. Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1100068
American Medical Association (AMA)
Wieczorek, Wojciech& Unold, Olgierd. Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100068
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100068