Multi-output interval type-2 fuzzy logic system for protein secondary structure prediction

Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, Multi-output interval type-2 fuzzy logic system for protein secondary structure prediction, International journal of uncertainty, fuzziness and knowlege-based systems, vol. 23, no. 5, pp. 735-760, doi: 10.1142/S0218488515500324.

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Title Multi-output interval type-2 fuzzy logic system for protein secondary structure prediction
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh orcid.org/0000-0001-9709-1663
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name International journal of uncertainty, fuzziness and knowlege-based systems
Volume number 23
Issue number 5
Start page 735
End page 760
Total pages 26
Publisher World Scientific Publishing
Place of publication Singapore
Publication date 2015
ISSN 0218-4885
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Interval type-2 fuzzy system
neural network
genetic algorithm
protein secondary structure
Chou-Fasman method
GOR method
amino acids
RULE-BASED CLASSIFIERS
SOLVENT EXPOSURE
SETS
CLASSIFICATION
RECOGNITION
FEATURES
VIEW
Summary A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.
Language eng
DOI 10.1142/S0218488515500324
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 970106 Expanding Knowledge in the Biological Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, World Scientific Publishing
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080141

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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