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Unravelling the Yeast Cell Cycle Using the TriGen Algorithm

Opened Access Unravelling the Yeast Cell Cycle Using the TriGen Algorithm


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Autor: Gutiérrez Avilés, David
Rubio Escudero, Cristina
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2011
Publicado en: Advances in Artificial Intelligence : 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, La Laguna, Spain, November 7-11, 2011. Proceedings. Lecture Notes in Computer Sciencie, v.7023
ISBN/ISSN: 978-3-642-25273-0
Tipo de documento: Capítulo de Libro
Resumen: Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping allowing genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of temporal microarray data in which the genes are evaluated under certain conditions at several time points. In this paper, we present the results of applying the TriGen algorithm, a genetic algorithm that finds triclusters that take into account the experimental conditions and the time points, to the yeast cell cycle problem, where the goal is to identify all genes whose expression levels are regulated by the cell cycle.
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