View in EDS HTML Full Text PDF Full Text

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.

Saved in:
Bibliographic Details
Title: RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.
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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 36607897
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Seal+S%22">Seal S</searchLink>; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America.<br /><searchLink fieldCode="AU" term="%22Li+Q%22">Li Q</searchLink>; Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, United States of America.<br /><searchLink fieldCode="AU" term="%22Basner+EB%22">Basner EB</searchLink>; Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, United States of America.<br /><searchLink fieldCode="AU" term="%22Saba+LM%22">Saba LM</searchLink>; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America.<br /><searchLink fieldCode="AU" term="%22Kechris+K%22">Kechris K</searchLink>; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101238922%22">PLoS computational biology</searchLink> [PLoS Comput Biol] 2023 Jan 06; Vol. 19 (1), pp. e1010758. <i>Date of Electronic Publication: </i>2023 Jan 06 (<i>Print Publication: </i>2023).
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article; Research Support, N.I.H., Extramural
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Public+Library+of+Science%22">Public Library of Science </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101238922 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>1553-7358 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%221553734X%22">1553734X </searchLink><i>NLM ISO Abbreviation: </i>PLoS Comput Biol <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=36607897
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1371/journal.pcbi.1010758
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: e1010758
    Titles:
      – TitleFull: RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Seal S
      – PersonEntity:
          Name:
            NameFull: Li Q
      – PersonEntity:
          Name:
            NameFull: Basner EB
      – PersonEntity:
          Name:
            NameFull: Saba LM
      – PersonEntity:
          Name:
            NameFull: Kechris K
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 06
              M: 01
              Text: 2023 Jan 06
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-electronic
              Value: 1553-7358
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: PLoS computational biology
              Type: main
ResultId 1