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Title: Obtención de redes de asociación directa con aplicación a datos ómicos
Author: Ainciburu Fernández, Marina
Tutor: Vegas Lozano, Esteban
Others: Sánchez-Pla, Alex
Keywords: GGM
Issue Date: 5-Jun-2018
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: microRNA (miRNA) are key genic regulators that inhibit translation and/or promote degradation of their target RNA. They take part in essential biological processes and play a role in various diseases. Thus, stablishing the miRNA ¿ genes regulation network can be important. Different computational methods have been developed to predict miRNA targets. In this study, we explore methods to infer direct associations from expression data coming from microarray experiments. In order to do this, we use algorithms that estimate partial correlations and allow us to create Gaussian Graphical Models (GGM) from high-dimensional data, where there are more variables than samples. We evaluate the performance of algorithms already implemented in R packages, by applying them to different kinds of data: small data, with n > p, simulated multivariate normal data, simulated genomic data and real mRNA and miRNA expression data. Results show how association networks created by GGM are useful in conditions with little variable and lots of samples. However, we become aware of the failure of these methods to infer associations as the number of variables grows bigger.
Language: Spanish
Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

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