2026-09-15T17:01:14Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1342802024-03-13T09:52:54Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243

00925njm 22002777a 4500 dc Ramos González, Juan author Castellanos Garzón, José Antonio author González Briones, Alfonso author De Paz, Juan F. author Corchado Rodríguez, Juan Manuel author 2017 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 multiagent 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. Interdisciplinary Sciences-Computational Life Sciences. Springer Verlag. http://hdl.handle.net/10366/134280 Computer Science An agent-based clustering approach for gene selection in gene expression microarray