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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • KTH Royal Institute of Technology
  • Broad Institute
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Hematologi
Originalspråkengelska
Sidor (från-till)511-512
TidskriftBioinformatics
Volym29
Utgivningsnummer4
Tidigt onlinedatum2012
StatusPublished - 2013
PublikationskategoriForskning
Peer review utfördJa

Relaterad forskningsoutput

Järvstråt, L., 2017, Lund: Lund University, Faculty of Medicine. 42 s.

Forskningsoutput: AvhandlingDoktorsavhandling (sammanläggning)

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