A Bayesian semi-parametric model for thermal proteome profiling

Fang et al. develop a Bayesian data analysis approach that is better suited to the analysis of Thermal Proteome Profiling (TPP) data than existing data analysis approaches that have limitations with respect to deviations from the expected sigmoid data behaviour. Their approach, which is more compreh...

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Autores principales:	Siqi Fang, Paul D. W. Kirk, Marcus Bantscheff, Kathryn S. Lilley, Oliver M. Crook
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/7779bba951a04cb6ad7afed9774db400
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id	oai:doaj.org-article:7779bba951a04cb6ad7afed9774db400
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spelling	oai:doaj.org-article:7779bba951a04cb6ad7afed9774db4002021-12-02T16:32:07ZA Bayesian semi-parametric model for thermal proteome profiling10.1038/s42003-021-02306-82399-3642https://doaj.org/article/7779bba951a04cb6ad7afed9774db4002021-06-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02306-8https://doaj.org/toc/2399-3642Fang et al. develop a Bayesian data analysis approach that is better suited to the analysis of Thermal Proteome Profiling (TPP) data than existing data analysis approaches that have limitations with respect to deviations from the expected sigmoid data behaviour. Their approach, which is more comprehensive and sensitive than standard data analysis methods, identified new putative targets and off-targets from published TPP datasets.Siqi FangPaul D. W. KirkMarcus BantscheffKathryn S. LilleyOliver M. CrookNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Siqi Fang Paul D. W. Kirk Marcus Bantscheff Kathryn S. Lilley Oliver M. Crook A Bayesian semi-parametric model for thermal proteome profiling
description	Fang et al. develop a Bayesian data analysis approach that is better suited to the analysis of Thermal Proteome Profiling (TPP) data than existing data analysis approaches that have limitations with respect to deviations from the expected sigmoid data behaviour. Their approach, which is more comprehensive and sensitive than standard data analysis methods, identified new putative targets and off-targets from published TPP datasets.
format	article
author	Siqi Fang Paul D. W. Kirk Marcus Bantscheff Kathryn S. Lilley Oliver M. Crook
author_facet	Siqi Fang Paul D. W. Kirk Marcus Bantscheff Kathryn S. Lilley Oliver M. Crook
author_sort	Siqi Fang
title	A Bayesian semi-parametric model for thermal proteome profiling
title_short	A Bayesian semi-parametric model for thermal proteome profiling
title_full	A Bayesian semi-parametric model for thermal proteome profiling
title_fullStr	A Bayesian semi-parametric model for thermal proteome profiling
title_full_unstemmed	A Bayesian semi-parametric model for thermal proteome profiling
title_sort	bayesian semi-parametric model for thermal proteome profiling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7779bba951a04cb6ad7afed9774db400
work_keys_str_mv	AT siqifang abayesiansemiparametricmodelforthermalproteomeprofiling AT pauldwkirk abayesiansemiparametricmodelforthermalproteomeprofiling AT marcusbantscheff abayesiansemiparametricmodelforthermalproteomeprofiling AT kathrynslilley abayesiansemiparametricmodelforthermalproteomeprofiling AT olivermcrook abayesiansemiparametricmodelforthermalproteomeprofiling AT siqifang bayesiansemiparametricmodelforthermalproteomeprofiling AT pauldwkirk bayesiansemiparametricmodelforthermalproteomeprofiling AT marcusbantscheff bayesiansemiparametricmodelforthermalproteomeprofiling AT kathrynslilley bayesiansemiparametricmodelforthermalproteomeprofiling AT olivermcrook bayesiansemiparametricmodelforthermalproteomeprofiling
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A Bayesian semi-parametric model for thermal proteome profiling

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