title: Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling.
abstract: CONCLUSIONS
  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.
  
  
  METHODS
  Ultranet is implemented in C++ and available at www.broadinstitute.org/ultranet.
authors.affiliation: Department of Hematology and Transfusion Medicine, Lund University Hospital, SE-221 85 Lund, Sweden.
authors.forename: Linnea
authors.initials: L
authors.lastname: Järvstråt
authors.forename: Mikael
authors.initials: M
authors.lastname: Johansson
authors.forename: Urban
authors.initials: U
authors.lastname: Gullberg
authors.forename: Björn
authors.initials: B
authors.lastname: Nilsson
mesh_terms: D053263:Gene Regulatory Networks; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D016011:Normal Distribution; D012984:Software; D059467:Transcriptome