Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data
Joint Authors
Bir-Jmel, Ahmed
Douiri, Sidi Mohamed
Elbernoussi, Souad
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-13
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
The recent advance in the microarray data analysis makes it easy to simultaneously measure the expression levels of several thousand genes.
These levels can be used to distinguish cancerous tissues from normal ones.
In this work, we are interested in gene expression data dimension reduction for cancer classification, which is a common task in most microarray data analysis studies.
This reduction has an essential role in enhancing the accuracy of the classification task and helping biologists accurately predict cancer in the body; this is carried out by selecting a small subset of relevant genes and eliminating the redundant or noisy genes.
In this context, we propose a hybrid approach (MWIS-ACO-LS) for the gene selection problem, based on the combination of a new graph-based approach for gene selection (MWIS), in which we seek to minimize the redundancy between genes by considering the correlation between the latter and maximize gene-ranking (Fisher) scores, and a modified ACO coupled with a local search (LS) algorithm using the classifier 1NN for measuring the quality of the candidate subsets.
In order to evaluate the proposed method, we tested MWIS-ACO-LS on ten well-replicated microarray datasets of high dimensions varying from 2308 to 12600 genes.
The experimental results based on ten high-dimensional microarray classification problems demonstrated the effectiveness of our proposed method.
American Psychological Association (APA)
Bir-Jmel, Ahmed& Douiri, Sidi Mohamed& Elbernoussi, Souad. 2019. Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-20.
https://search.emarefa.net/detail/BIM-1130712
Modern Language Association (MLA)
Bir-Jmel, Ahmed…[et al.]. Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-20.
https://search.emarefa.net/detail/BIM-1130712
American Medical Association (AMA)
Bir-Jmel, Ahmed& Douiri, Sidi Mohamed& Elbernoussi, Souad. Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-20.
https://search.emarefa.net/detail/BIM-1130712
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130712