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

Summary, in English

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. CONTACT: bnilsson [at] broadinstitute [dot] org, bjorn [dot] nilsson [at] med [dot] lu [dot] se.

• Author: Linnea Järvstråt; M Johansson; Urban Gullberg; B. Nilsson
• Department/s: Division of Hematology and Transfusion Medicine; Hematogenomics; BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
• Publishing year: 2013
• Language: English
• Pages: 511-512
• Publication/Series: Bioinformatics
• Volume: 29
• Issue: 4
• Links: http://www.ncbi.nlm.nih.gov/pubmed/23267175?dopt=Abstract; http://dx.doi.org/10.1093/bioinformatics/bts717
• Document type: Journal article
• Publisher: Oxford University Press
• Topic: Hematology
• Status: Published
• Research group: Hematogenomics
• ISBN/ISSN/Other: ISSN: 1367-4803