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Structural classification of proteins through amino acid sequence using interval type-2 fuzzy logic system
conference contribution
posted on 2023-10-26, 04:17 authored by Thanh Thi NguyenThanh Thi Nguyen, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, Saeid NahavandiThis paper introduces a new multi-output interval type-2 fuzzy logic system (MOIT2FLS) that is automatically constructed from unsupervised data clustering method and trained using heuristic genetic algorithm for a protein secondary structure classification. Three structure classes are distinguished including helix, strand (sheet) and coil which correspond to three outputs of the MOIT2FLS. Quantitative properties of amino acids are used to characterize the twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Amino acid sequences are parsed into learnable patterns using a local moving window strategy. Three clustering tasks are performed using the adaptive vector quantization method to derive an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS with the purpose of maximizing the Q3 measure. Comprehensive experimental results demonstrate the strong superiority of the proposed approach over the traditional methods including Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.
History
Pagination
1123 - 1130Location
Beijing, ChinaPublisher DOI
Start date
2014-07-06End date
2014-07-11ISSN
1098-7584ISBN-13
9780000000000.0Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEETitle of proceedings
Proceedings of the 2014 IEEE International Conference on Fuzzy Systems: FUZZ-IEEE 2014Usage metrics
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