View in EDS HTML Full Text PDF Full Text

Predicting congenital renal tract malformation genes using machine learning.

Saved in:
Bibliographic Details
Title: Predicting congenital renal tract malformation genes using machine learning.
Authors: Kabir M; CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK., Stuart HM; CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.; Manchester Centre for Genomic Medicine, St. Mary's Hospital, Health Innovation Manchester, Manchester University Foundation NHS Trust, Manchester, M13 9WL, UK., Lopes FM; Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK., Fotiou E; Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK.; C.B.B Lifeline Biotech Ltd, 5 Propontidos Street, Strovolos, 2033, Nicosia, Cyprus., Keavney B; Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK.; Manchester Heart Institute, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK., Doig AJ; Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Stopford Building, Manchester, M13 9BL, UK., Woolf AS; Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK.; Department of Nephrology, Royal Manchester Children's Hospital, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK., Hentges KE; CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK. Kathryn.hentges@manchester.ac.uk.
Source: Scientific reports [Sci Rep] 2023 Aug 14; Vol. 13 (1), pp. 13204. Date of Electronic Publication: 2023 Aug 14.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep 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: 37580336
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predicting congenital renal tract malformation genes using machine learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Kabir+M%22">Kabir M</searchLink>; CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.<br /><searchLink fieldCode="AU" term="%22Stuart+HM%22">Stuart HM</searchLink>; CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.; Manchester Centre for Genomic Medicine, St. Mary's Hospital, Health Innovation Manchester, Manchester University Foundation NHS Trust, Manchester, M13 9WL, UK.<br /><searchLink fieldCode="AU" term="%22Lopes+FM%22">Lopes FM</searchLink>; Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK.<br /><searchLink fieldCode="AU" term="%22Fotiou+E%22">Fotiou E</searchLink>; Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK.; C.B.B Lifeline Biotech Ltd, 5 Propontidos Street, Strovolos, 2033, Nicosia, Cyprus.<br /><searchLink fieldCode="AU" term="%22Keavney+B%22">Keavney B</searchLink>; Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK.; Manchester Heart Institute, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK.<br /><searchLink fieldCode="AU" term="%22Doig+AJ%22">Doig AJ</searchLink>; Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Stopford Building, Manchester, M13 9BL, UK.<br /><searchLink fieldCode="AU" term="%22Woolf+AS%22">Woolf AS</searchLink>; Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK.; Department of Nephrology, Royal Manchester Children's Hospital, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK.<br /><searchLink fieldCode="AU" term="%22Hentges+KE%22">Hentges KE</searchLink>; CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK. Kathryn.hentges@manchester.ac.uk.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101563288%22">Scientific reports</searchLink> [Sci Rep] 2023 Aug 14; Vol. 13 (1), pp. 13204. <i>Date of Electronic Publication: </i>2023 Aug 14.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article; Research Support, Non-U.S. Gov't
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Publishing+Group%22">Nature Publishing Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101563288 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2045-2322 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220452322%22">20452322 </searchLink><i>NLM ISO Abbreviation: </i>Sci Rep <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=37580336
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1038/s41598-023-38110-z
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 13204
    Titles:
      – TitleFull: Predicting congenital renal tract malformation genes using machine learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kabir M
      – PersonEntity:
          Name:
            NameFull: Stuart HM
      – PersonEntity:
          Name:
            NameFull: Lopes FM
      – PersonEntity:
          Name:
            NameFull: Fotiou E
      – PersonEntity:
          Name:
            NameFull: Keavney B
      – PersonEntity:
          Name:
            NameFull: Doig AJ
      – PersonEntity:
          Name:
            NameFull: Woolf AS
      – PersonEntity:
          Name:
            NameFull: Hentges KE
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 14
              M: 08
              Text: 2023 Aug 14
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-electronic
              Value: 2045-2322
          Numbering:
            – Type: volume
              Value: 13
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Scientific reports
              Type: main
ResultId 1