FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.

ABSTRACT: Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et

SUBMITTER: Wang T 

PROVIDER: S-EPMC4752261 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature