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Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling
KTH, School of Electrical Engineering (EES), Automatic Control.
2013 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 29, no 4, 511-512 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 29, no 4, 511-512 p.
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:kth:diva-119454DOI: 10.1093/bioinformatics/bts717ISI: 000315158500018ScopusID: 2-s2.0-84874322384OAI: diva2:611242
Swedish Foundation for Strategic Research , ICA08-0057

QC 20130315

Available from: 2013-03-15 Created: 2013-03-14 Last updated: 2013-03-15Bibliographically approved

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