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A novel aggregate gene selection method for microarray data classification

Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, A novel aggregate gene selection method for microarray data classification, Pattern recognition letters, vol. 60-61, pp. 16-23, doi: 10.1016/j.patrec.2015.03.018.

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Title A novel aggregate gene selection method for 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, Saeid
Journal name Pattern recognition letters
Volume number 60-61
Start page 16
End page 23
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-08-01
ISSN 0167-8655
Keyword(s) Analytic hierarchy process
Classification
Gene expression profiles
Gene selection
Microarray data
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
DIFFERENTIALLY EXPRESSED GENES
CANCER CLASSIFICATION
SPARSE
Summary This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.
Language eng
DOI 10.1016/j.patrec.2015.03.018
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074928

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Created: Mon, 10 Aug 2015, 11:16:47 EST

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