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Bioinformatics (Oxford, England) 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 (Oxford, England)
Date Published2013/01/21

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 CONTACT: or SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.