Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling.

Bioinformatics
Authors
Keywords
Abstract

SUMMARY: Graphical Gaussian models (GGMs) are a promising approach to identify gene regulatory networks. Such models can be robustly inferred by solving the sparse inverse covariance selection (SICS) problem. With the high dimensionality of genomics data, fast methods capable of solving large instances of SICS are needed. We developed a novel network modeling tool, Ultranet, that solves the SICS problem with significantly improved efficiency. Ultranet combines a range of mathematical and programmatical techniques, exploits the structure of the SICS problem and enables computation of genome-scale GGMs without compromising analytic accuracy.

AVAILABILITY AND IMPLEMENTATION: Ultranet is implemented in C++ and available at www.broadinstitute.org/ultranet.

Year of Publication
2013
Journal
Bioinformatics
Volume
29
Issue
4
Pages
511-2
Date Published
2013 Feb 15
ISSN
1367-4811
URL
DOI
10.1093/bioinformatics/bts717
PubMed ID
23267175
Links