MCPeSe: Monte Carlo penalty selection for graphical lasso.

ABSTRACT:

Motivation

Graphical lasso (Glasso) is a widely used tool for identifying gene regulatory networks in systems biology. However, its computational efficiency depends on the choice of regularization parameter (tuning parameter), and selecting this parameter can be highly time consuming. Although fully Bayesian implementations of Glasso alleviate this problem somewhat by specifying a priori distribution for the parameter, these approaches lack the scalability of their frequentist counterparts.

Results

Here, we present a new Monte Carlo Penalty Selection method (MCPeSe), a computationally efficient approach to regularization parameter selection for Glasso. MCPeSe combines the scalability and low computational cost of the frequentist Glasso with the ability to automatically choos

SUBMITTER: Kuismin M 

PROVIDER: S-EPMC8097680 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature