Openly accessible

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification, PLoS one, vol. 10, no. 3, Article Number : e0120364, pp. 1-23, doi: 10.1371/journal.pone.0120364.

Attached Files
Name Description MIMEType Size Downloads
nguyen-hierarchicalgene-2015.pdf Published version application/pdf 1.22MB 104

Title Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification
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 PLoS one
Volume number 10
Issue number 3
Season Article Number : e0120364
Start page 1
End page 23
Total pages 23
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2015
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
FUNCTION APPROXIMATION
PREDICTION
ALGORITHM
SETS
TOOL
Summary This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.
Notes Reproduced with the kind permission of the copyright owner.
Language eng
DOI 10.1371/journal.pone.0120364
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075812

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 9 times in TR Web of Science
Scopus Citation Count Cited 13 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 283 Abstract Views, 105 File Downloads  -  Detailed Statistics
Created: Thu, 20 Aug 2015, 14:03:36 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.