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[88760] Artykuł: Gene-Promoter-Sequence Recognition – an Interpretable and Accurate Fuzzy-Genetic ApproachCzasopismo: IEEE International Conference on Fuzzy Systems Strony: 1468-1473ISSN: 1558-4739 ISBN: 978-1-5386-1728-1 Opublikowano: 2019 Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Konferencja Informatyczna Punkty MNiSW: 140 Klasyfikacja Web of Science: Proceedings Paper DOI Keywords: bioinformatics  gene-promoter-sequence recognition  fuzzy rule-based classifiers  multi-objective evolutionary optimization  accuracy-interpretability trade-off optimization  |
Different methods applied to gene promoter recognition share, in general, the same drawback, i.e., they are non-interpretable black-box-type techniques. The main objective of this paper is the application of our fuzzy rule-based classification approach characterized by genetically optimized accuracy-interpretability trade off (using multi-objective evolutionary optimization algorithms (M-OEOAs)) to gene promoter recognition. Two publicly accessible bacterial DNA benchmark data sets, i.e., Molecular Biology (Promoter Gene Sequences) and iPromoter-FSEn benchmark data sets are considered. For comparison purposes, two M-OEOAs are used in our experiments, i.e., the well-known Strength Pareto Evolutionary Algorithm 2 (SPEA2) and our generalization of SPEA2 (referred to as SPEA3) characterized by a higher spread and better-balanced distribution of solutions. Our results for both considered molecular biology data sets are compared with the results of 16 alternative methods (including several state-of-the-art ones) demonstrating the advantages – in terms of system’s accuracy-interpretability trade-off optimization – of our approach.