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Hidden Markov models for cancer classification using gene expression profiles

Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, Hidden Markov models for cancer classification using gene expression profiles, Information sciences, vol. 316, pp. 293-307, doi: 10.1016/j.ins.2015.04.012.

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Title Hidden Markov models for cancer classification using gene expression profiles
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 Information sciences
Volume number 316
Start page 293
End page 307
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-09-20
ISSN 0020-0255
Keyword(s) Analytic hierarchy process
Cancer classification
DNA microarray
Gene expression profile
Gene selection
Hidden Markov model
Science & Technology
Technology
Computer Science, Information Systems
Computer Science
PARTIAL LEAST-SQUARES
MICROARRAY DATA
PROBABILISTIC FUNCTIONS
NEURAL NETWORKS
SELECTION
MULTICLASS
PREDICTION
CHAINS
Summary This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.
Language eng
DOI 10.1016/j.ins.2015.04.012
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:30075806

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Created: Wed, 26 Aug 2015, 09:35:56 EST

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