Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.
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| Title: | Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity. |
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| Authors: | Gardiner LJ; IBM Research UK, Sci-Tech Daresbury, Warrington, UK. Laura-Jayne.Gardiner@ibm.com., Carrieri AP; IBM Research UK, Sci-Tech Daresbury, Warrington, UK., Wilshaw J; IBM Research UK, Sci-Tech Daresbury, Warrington, UK., Checkley S; STFC Daresbury Lab., Warrington, UK., Pyzer-Knapp EO; IBM Research UK, Sci-Tech Daresbury, Warrington, UK., Krishna R; IBM Research UK, Sci-Tech Daresbury, Warrington, UK. Ritesh.krishna@uk.ibm.com. |
| Source: | Scientific reports [Sci Rep] 2020 Jun 12; Vol. 10 (1), pp. 9522. Date of Electronic Publication: 2020 Jun 12. |
| 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 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 32533004 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Gardiner+LJ%22">Gardiner LJ</searchLink>; IBM Research UK, Sci-Tech Daresbury, Warrington, UK. Laura-Jayne.Gardiner@ibm.com.<br /><searchLink fieldCode="AU" term="%22Carrieri+AP%22">Carrieri AP</searchLink>; IBM Research UK, Sci-Tech Daresbury, Warrington, UK.<br /><searchLink fieldCode="AU" term="%22Wilshaw+J%22">Wilshaw J</searchLink>; IBM Research UK, Sci-Tech Daresbury, Warrington, UK.<br /><searchLink fieldCode="AU" term="%22Checkley+S%22">Checkley S</searchLink>; STFC Daresbury Lab., Warrington, UK.<br /><searchLink fieldCode="AU" term="%22Pyzer-Knapp+EO%22">Pyzer-Knapp EO</searchLink>; IBM Research UK, Sci-Tech Daresbury, Warrington, UK.<br /><searchLink fieldCode="AU" term="%22Krishna+R%22">Krishna R</searchLink>; IBM Research UK, Sci-Tech Daresbury, Warrington, UK. Ritesh.krishna@uk.ibm.com. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101563288%22">Scientific reports</searchLink> [Sci Rep] 2020 Jun 12; Vol. 10 (1), pp. 9522. <i>Date of Electronic Publication: </i>2020 Jun 12. – 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=32533004 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41598-020-66481-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 9522 Titles: – TitleFull: Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gardiner LJ – PersonEntity: Name: NameFull: Carrieri AP – PersonEntity: Name: NameFull: Wilshaw J – PersonEntity: Name: NameFull: Checkley S – PersonEntity: Name: NameFull: Pyzer-Knapp EO – PersonEntity: Name: NameFull: Krishna R IsPartOfRelationships: – BibEntity: Dates: – D: 12 M: 06 Text: 2020 Jun 12 Type: published Y: 2020 Identifiers: – Type: issn-electronic Value: 2045-2322 Numbering: – Type: volume Value: 10 – Type: issue Value: 1 Titles: – TitleFull: Scientific reports Type: main |
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