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

Linnea Järvstråt, M Johansson, Urban Gullberg, B. Nilsson

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

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.
Originalspråkengelska
Sidor (från-till)511-512
TidskriftBioinformatics
Volym29
Nummer4
Tidigt onlinedatum2012
DOI
StatusPublished - 2013

Ämnesklassifikation (UKÄ)

  • Hematologi

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