RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.
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| Title: | RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks. |
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| Authors: | Seal S; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America., Li Q; Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, United States of America., Basner EB; Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, United States of America., Saba LM; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America., Kechris K; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America. |
| Source: | PLoS computational biology [PLoS Comput Biol] 2023 Jan 06; Vol. 19 (1), pp. e1010758. Date of Electronic Publication: 2023 Jan 06 (Print Publication: 2023). |
| Publication Type: | Journal Article; Research Support, N.I.H., Extramural |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1553-7358 |
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| DOI: | 10.1371/journal.pcbi.1010758 |
