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Related Experiment Video

Updated: May 15, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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Published on: December 7, 2021

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

Linnea Järvstråt1, Mikael Johansson, Urban Gullberg

  • 1Department of Hematology and Transfusion Medicine, Lund University Hospital, SE-221 85 Lund, Sweden.

Bioinformatics (Oxford, England)
|December 26, 2012
PubMed
Summary

We developed Ultranet, a novel tool for identifying gene regulatory networks using graphical Gaussian models (GGMs). Ultranet efficiently solves the sparse inverse covariance selection problem for large-scale genomics data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Network Biology

Background:

  • Graphical Gaussian models (GGMs) are effective for inferring gene regulatory networks.
  • Inferring GGMs involves solving the sparse inverse covariance selection (SICS) problem.
  • High-dimensional genomics data necessitates computationally efficient SICS methods.

Purpose of the Study:

  • To develop a novel, efficient tool for solving the SICS problem.
  • To enable the computation of genome-scale GGMs.
  • To address the need for fast methods in high-dimensional genomics data analysis.

Main Methods:

  • Developed Ultranet, a C++ implemented network modeling tool.
  • Ultranet combines mathematical and programmatical techniques.
  • Exploits the inherent structure of the SICS problem for improved efficiency.

Main Results:

  • Ultranet significantly improves efficiency in solving the SICS problem.
  • Enables computation of genome-scale GGMs.
  • Maintains analytic accuracy in network inference.

Conclusions:

  • Ultranet is a promising tool for identifying gene regulatory networks.
  • Offers a fast and accurate solution for large-scale SICS problems in genomics.
  • Facilitates robust inference of complex biological networks.