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. Attached Files Name Description MIMEType Size Downloads Title Hidden Markov models for cancer classification using gene expression profiles Author(s) Nguyen, Thanh orcid.org/0000-0001-9709-1663 Khosravi, Abbas orcid.org/0000-0001-6927-0744 Creighton, Douglas orcid.org/0000-0002-9217-1231 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 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 Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 20 Aug 2015, 14:00:59 EST Fri, 21 Aug 2015, 05:06:09 EST Wed, 26 Aug 2015, 09:35:56 EST Thu, 27 Aug 2015, 06:08:39 EST Thu, 27 Aug 2015, 12:12:36 EST Thu, 27 Aug 2015, 12:14:14 EST Filtered Full Citation counts:   Cited 12 times in TR Web of Science Cited 13 times in Scopus Search Google Scholar Access Statistics: 297 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 26 Aug 2015, 09:35:56 EST

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