A Bayesian mixture modelling approach for spatial proteomics.
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| Title: | A Bayesian mixture modelling approach for spatial proteomics. |
|---|---|
| Authors: | Crook, Oliver M.1,2,3, Mulvey, Claire M.2, Kirk, Paul D. W.3, Lilley, Kathryn S.2, Gatto, Laurent1,2 laurent.gatto@uclouvain.be |
| Source: | PLoS Computational Biology. 11/27/2018, Vol. 14 Issue 11, p1-29. 29p. 1 Diagram, 1 Chart, 11 Graphs. |
| Database: | Academic Search Ultimate |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: asn DbLabel: Academic Search Ultimate An: 133225297 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=133225297 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1371/journal.pcbi.1006516 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1 Titles: – TitleFull: A Bayesian mixture modelling approach for spatial proteomics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Crook, Oliver M. – PersonEntity: Name: NameFull: Mulvey, Claire M. – PersonEntity: Name: NameFull: Kirk, Paul D. W. – PersonEntity: Name: NameFull: Lilley, Kathryn S. – PersonEntity: Name: NameFull: Gatto, Laurent IsPartOfRelationships: – BibEntity: Dates: – D: 27 M: 11 Text: 11/27/2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 1553734X Numbering: – Type: volume Value: 14 – Type: issue Value: 11 Titles: – TitleFull: PLoS Computational Biology Type: main |
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