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Bioinformatics DOI:10.1093/bioinformatics/bts717

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

Publication TypeJournal Article
Year of Publication2013
AuthorsJärvstråt, L, Johansson, M, Gullberg, U, Nilsson, B
JournalBioinformatics
Volume29
Issue4
Pages511-2
Date Published2013 Feb 15
ISSN1367-4811
KeywordsGene Regulatory Networks, Genomics, Humans, Models, Genetic, Normal Distribution, Software, Transcriptome
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.

URLhttp://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=23267175
DOI10.1093/bioinformatics/bts717
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/23267175?dopt=Abstract

Alternate JournalBioinformatics
PubMed ID23267175