- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1474-760X
- Extent
-
Online-Ressource
- Language
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Englisch
- Notes
-
online resource.
- Bibliographic citation
-
A statistical framework for analyzing deep mutational scanning data ; volume:18 ; number:1 ; day:7 ; month:8 ; year:2017 ; pages:1-15 ; date:12.2017
Genome biology ; 18, Heft 1 (7.8.2017), 1-15, 12.2017
- Classification
-
Biowissenschaften, Biologie
- Creator
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Rubin, Alan F.
- Contributor
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Gelman, Hannah
Lucas, Nathan
Bajjalieh, Sandra M.
Papenfuss, Anthony T.
Speed, Terence P.
Fowler, Douglas M.
SpringerLink (Online service)
- DOI
-
10.1186/s13059-017-1272-5
- URN
-
urn:nbn:de:1111-201708182546
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:27 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Rubin, Alan F.
- Gelman, Hannah
- Lucas, Nathan
- Bajjalieh, Sandra M.
- Papenfuss, Anthony T.
- Speed, Terence P.
- Fowler, Douglas M.
- SpringerLink (Online service)
Other Objects (12)
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Author Correction: Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning
Statistical methods for analyzing immunosignatures
Mutational signatures: experimental design and analytical framework
Updated benchmarking of variant effect predictors using deep mutational scanning
Optimization of the antimicrobial peptide Bac7 by deep mutational scanning
Variant effect predictions capture some aspects of deep mutational scanning experiments
Adapting Habermas’ construct of communicative rationality into a framework for analyzing students’ statistical literacy
Correction to: A statistical framework for analyzing deep mutational scanning data
Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage
Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning
Mutational scanning of CLOCK exon 19 transactivation domain
DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies
Author Correction: Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning
Statistical methods for analyzing immunosignatures
Mutational signatures: experimental design and analytical framework
Updated benchmarking of variant effect predictors using deep mutational scanning
Optimization of the antimicrobial peptide Bac7 by deep mutational scanning
Variant effect predictions capture some aspects of deep mutational scanning experiments
Adapting Habermas’ construct of communicative rationality into a framework for analyzing students’ statistical literacy
Correction to: A statistical framework for analyzing deep mutational scanning data
Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage
Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning
Mutational scanning of CLOCK exon 19 transactivation domain
DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies
Author Correction: Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning
Statistical methods for analyzing immunosignatures
Mutational signatures: experimental design and analytical framework
Updated benchmarking of variant effect predictors using deep mutational scanning
Optimization of the antimicrobial peptide Bac7 by deep mutational scanning
Variant effect predictions capture some aspects of deep mutational scanning experiments