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

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Version 2 2024-06-05, 11:48
Version 1 2015-08-20, 14:03
journal contribution
posted on 2024-06-05, 11:48 authored by T Nguyen, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, S Nahavandi
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.

History

Journal

PLoS one

Volume

10

Season

Article Number : e0120364

Pagination

1-23

Location

San Francisco, Calif.

Open access

  • Yes

ISSN

1932-6203

Language

eng

Notes

Reproduced with the kind permission of the copyright owner.

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2015, The Authors

Issue

3

Publisher

Public Library of Science