Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling.
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3346900
- author
- Järvstråt, Linnea LU ; Johansson, M ; Gullberg, Urban LU and Nilsson, B.
- organization
- publishing date
- 2013
- 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
- pmid:23267175
- 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
- 2016-04-04 07:06:57
- date last changed
- 2022-02-05 22:01:59
@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}}, doi = {{10.1093/bioinformatics/bts717}}, volume = {{29}}, year = {{2013}}, }