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

Järvstråt, Linnea LU ; Johansson, M; Gullberg, Urban LU and Nilsson, B. (2013) In Bioinformatics 29(4). p.511-512
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... (More)
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@broadinstitute.org, bjorn.nilsson@med.lu.se. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Bioinformatics
volume
29
issue
4
pages
511 - 512
publisher
Oxford University Press
external identifiers
  • wos:000315158500018
  • pmid:23267175
  • scopus:84874322384
ISSN
1367-4803
DOI
10.1093/bioinformatics/bts717
language
English
LU publication?
yes
id
40e62e36-b1aa-4261-a41a-e3fdbdf341ad (old id 3346900)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/23267175?dopt=Abstract
date added to LUP
2013-01-02 13:12:59
date last changed
2019-03-13 12:04:58
@article{40e62e36-b1aa-4261-a41a-e3fdbdf341ad,
  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. CONTACT: bnilsson@broadinstitute.org, bjorn.nilsson@med.lu.se.},
  author       = {Järvstråt, Linnea and Johansson, M and Gullberg, Urban and Nilsson, B.},
  issn         = {1367-4803},
  language     = {eng},
  number       = {4},
  pages        = {511--512},
  publisher    = {Oxford University Press},
  series       = {Bioinformatics},
  title        = {Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling.},
  url          = {http://dx.doi.org/10.1093/bioinformatics/bts717},
  volume       = {29},
  year         = {2013},
}