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Inferring gene regression networks with model trees

Opened Access Inferring gene regression networks with model trees

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Autor: Nepomuceno Chamorro, Isabel de los Ángeles
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2010
Publicado en: BMC Bioinformatics, 11, 517-.
Tipo de documento: Artículo
Resumen: Background: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results: We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false di...
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Cita: Nepomuceno Chamorro, I.d.l.Á., Aguilar Ruiz, J.S. y Riquelme Santos, J.C. (2010). Inferring gene regression networks with model trees. BMC Bioinformatics, 11, 517-.
Tamaño: 2.079Mb
Formato: PDF

URI: http://hdl.handle.net/11441/43335

DOI: http://dx.doi.org/10.1186/1471-2105-11-517

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