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

• 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Hematologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Hematology (hsv//eng)

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art (subject category)