The Graph-guided Group Lasso
published: Aug. 26, 2013, recorded: July 2013, views: 4028
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this work we propose a penalised regression model in which the covariates are known to be clustered into groups, and the clusters are arranged as nodes in a graph. We are motivated by an application to genome-wide association studies in which the objective is to identify important predictors, single nucleotide polymorphisms (SNPs), that account for the variability of a quantitative trait. In this applications, SNPs naturally cluster into SNP sets representing genes, and genes are treated as nodes of a biological network encoding the functional relatedness of genes. Our proposed graph-guided group lasso (GGGL) takes into account such prior knowledge available on the covariates at two different levels, and allows to select important SNPs sets while also favouring the selection of functionally related genes. We describe a computationally efficient algorithm for parameter estimation, provide experimental results and present a GWA study on lipids levels in two Asian populations.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !