Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

Nguyen, Thanh, Nahavandi, Saeid, Khosravi, Abbas and Creighton, Douglas 2015, Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system, in UZZ-IEEE 2015: Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-8, doi: 10.1109/FUZZ-IEEE.2015.7338078.

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Title Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh orcid.org/0000-0001-9709-1663
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Conference name IEEE International Conference on Fuzzy Systems (2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 2-5 Aug. 2015
Title of proceedings UZZ-IEEE 2015: Proceedings of the IEEE International Conference on Fuzzy Systems
Editor(s) Yazici, A.
Pal, N. R.
Kaymak, U.
Martin, T.
Ishibuchi, H.
Lin, C. T.
Sousa, J. M. C.
Tutmez, B.
Publication date 2015
Series IEEE International Fuzzy Systems Conference Proceedings
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Cancer diagnosis
mass spectrometry
wavelet transform
Wilcoxon test
interval type-2 fuzzy logic system
tabu search
LOGIC SYSTEMS
SAMPLE CLASSIFICATION
BIOMARKER DISCOVERY
CLINICAL PROTEOMICS
SETS
DEFUZZIFICATION
REDUCTION
ALGORITHM
SELECTION
Summary An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.
ISBN 9781467374286
ISSN 1544-5615
Language eng
DOI 10.1109/FUZZ-IEEE.2015.7338078
Field of Research 080111 Virtual Reality and Related Simulation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083084

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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