Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling.
Författare
Summary, in English
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 [at] broadinstitute [dot] org, bjorn [dot] nilsson [at] med [dot] lu [dot] se.
Avdelning/ar
- Avdelningen för hematologi och transfusionsmedicin
- Hematogenomics
- BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
Publiceringsår
2013
Språk
Engelska
Sidor
511-512
Publikation/Tidskrift/Serie
Bioinformatics
Volym
29
Issue
4
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Oxford University Press
Ämne
- Hematology
Status
Published
Forskningsgrupp
- Hematogenomics
ISBN/ISSN/Övrigt
- ISSN: 1367-4803