Deakin University
Browse

File(s) under permanent embargo

Hidden Markov models for cancer classification using gene expression profiles

Version 2 2024-06-05, 11:48
Version 1 2015-08-20, 14:00
journal contribution
posted on 2024-06-05, 11:48 authored by T Nguyen, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, S Nahavandi
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.

History

Journal

Information sciences

Volume

316

Pagination

293-307

Location

Amsterdam, The Netherlands

ISSN

0020-0255

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2015, Elsevier

Publisher

Elsevier