An Agent-Based Clustering Approach for Gene Selection in Gene Expression Microarray

被引：17

作者：

Ramos, Juan ^{[1
]}

Castellanos-Garzon, Jose A. ^{[1
,2
]}

Gonzalez-Briones, Alfonso ^{[1
]}

de Paz, Juan F. ^{[1
]}

Corchado, Juan M. ^{[1
,3
]}

机构：

[1] Univ Salamanca, IBSAL BISITE Res Grp, Edificio I D I, Salamanca 37007, Spain

[2] Univ Coimbra, CISUC, ECOS Res Grp, Polo 2, P-3030290 Coimbra, Portugal

[3] Osaka Inst Technol, Osaka 5358585, Japan

来源：

INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES | 2017年 / 9卷 / 01期

关键词：

Gene selection; Filter method; Multi-agent system; Clustering; Classification; Machine learning; Visual analytics; DNA-microarray; LUNG-CANCER; CLASSIFICATION; ALGORITHMS; PATTERNS;

D O I：

10.1007/s12539-017-0219-6

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Gene selection is a major research area in microarray analysis, which seeks to discover differentially expressed genes for a particular target annotation. Such genes also often called informative genes are able to differentiate tissue samples belonging to different classes of the studied disease. Despite the fact that there is a wide number of proposals, the complexity imposed by this problem remains a challenge today. This research proposes a gene selection approach by means of a clustering-based multi-agent system. This proposal manages different filter methods and gene clustering through coordinated agents to discover informative gene subsets. To assess the reliability of our approach, we have used four important and public gene expression datasets, two Lung cancer datasets, Colon and Leukemia cancer dataset. The achieved results have been validated through cluster validity measures, visual analytics, a classifier and compared with other gene selection methods, proving the reliability of our proposal.

引用

页码：1 / 13

页数：13

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