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

Linnea Järvstråt | Zendy; Mikael Johansson | Zendy; Urban Gullberg | Zendy; Björn Nilsson | Zendy

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

Author(s) -

Linnea Järvstråt,

Mikael Johansson,

Urban Gullberg,

Björn Nilsson

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts717

Subject(s) - covariance , computer science , curse of dimensionality , solver , computation , selection (genetic algorithm) , graphical model , inverse , range (aeronautics) , estimation of covariance matrices , exploit , gaussian , data mining , mathematical optimization , algorithm , machine learning , covariance matrix , mathematics , statistics , physics , quantum mechanics , programming language , materials science , geometry , computer security , composite material

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.

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