{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T05:21:46Z","timestamp":1674969706111},"reference-count":9,"publisher":"Oxford University Press (OUP)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Availability and implementation: Ultranet is implemented in C++ and available at www.broadinstitute.org\/ultranet.<\/jats:p>\n               <jats:p>Contact: \u00a0bnilsson@broadinstitute.org or bjorn.nilsson@med.lu.se<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts717","type":"journal-article","created":{"date-parts":[[2012,12,25]],"date-time":"2012-12-25T03:12:57Z","timestamp":1356405177000},"page":"511-512","source":"Crossref","is-referenced-by-count":6,"title":["Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling"],"prefix":"10.1093","volume":"29","author":[{"given":"Linnea","family":"J\u00e4rvstr\u00e5t","sequence":"first","affiliation":[{"name":"1 Department of Hematology and Transfusion Medicine, Lund University Hospital, SE-221 85 Lund, Sweden, 2Department of Automatic Control, Royal Institute of Technology, SE-100 44 Stockholm, Sweden and 3Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA"}]},{"given":"Mikael","family":"Johansson","sequence":"additional","affiliation":[{"name":"1 Department of Hematology and Transfusion Medicine, Lund University Hospital, SE-221 85 Lund, Sweden, 2Department of Automatic Control, Royal Institute of Technology, SE-100 44 Stockholm, Sweden and 3Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA"}]},{"given":"Urban","family":"Gullberg","sequence":"additional","affiliation":[{"name":"1 Department of Hematology and Transfusion Medicine, Lund University Hospital, SE-221 85 Lund, Sweden, 2Department of Automatic Control, Royal Institute of Technology, SE-100 44 Stockholm, Sweden and 3Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA"}]},{"given":"Bj\u00f6rn","family":"Nilsson","sequence":"additional","affiliation":[{"name":"1 Department of Hematology and Transfusion Medicine, Lund University Hospital, SE-221 85 Lund, Sweden, 2Department of Automatic Control, Royal Institute of Technology, SE-100 44 Stockholm, Sweden and 3Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA"},{"name":"1 Department of Hematology and Transfusion Medicine, Lund University Hospital, SE-221 85 Lund, Sweden, 2Department of Automatic Control, Royal Institute of Technology, SE-100 44 Stockholm, Sweden and 3Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA"}]}],"member":"286","published-online":{"date-parts":[[2012,12,24]]},"reference":[{"key":"2023012810251133800_bts717-B1","article-title":"Projected subgradient methods for learning sparse Gaussians","author":"Duchi","year":"2008","journal-title":"Proceedings of the Conference Uncertainty in Artificial Intelligence"},{"key":"2023012810251133800_bts717-B2","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","article-title":"Sparse inverse covariance estimation with the graphical lasso","volume":"9","author":"Friedman","year":"2008","journal-title":"Biostatistics"},{"key":"2023012810251133800_bts717-B3","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1200\/JCO.2009.23.4732","article-title":"Clinical utility of microarray-based gene expression profiling in the diagnosis and classification of leukemia","volume":"28","author":"Haferlach","year":"2010","journal-title":"J. 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