2026-09-14T20:16:24Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1349672025-05-28T12:38:35Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811

González Peña, Daniel Díaz Gómez, Fernando Méndez, Jose R. Corchado Rodríguez, Juan Manuel Fernández Riverola, Florentino 2017-09-06T09:15:18Z 2017-09-06T09:15:18Z 2009-06 Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5518, pp. 972-978. 978-3-642-02480-1 (Print) / 978-3-642-02481-8 (Online) 0302-9743 (Print) / 1611-3349 (Online) http://hdl.handle.net/10366/134967 Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to more tailored therapies for patients as well as better diagnostic procedures. In this work, we present a novel method for automatically discovering clusters of samples which are coherent from a genetic point of view. Each possible cluster is characterized by a fuzzy pattern which maintains a fuzzy discretization of relevant gene expression values. Noise genes are identified and removed from the fuzzy pattern based on their probability of appearance. Possible clusters are randomly constructed and iteratively refined by following a probabilistic search and an optimization schema. Experimental results over publicly available microarray data show the effectiveness of the proposed method. en https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivs 3.0 Unported Computer Science An Evolutionary Approach for Sample-Based Clustering on Microarray Data info:eu-repo/semantics/conferenceObject