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Challenges and opportunities for Bayesian statistics in proteomics

Abstract:

Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributi...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1021/acs.jproteome.1c00859

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Publisher:
American Chemical Society
Journal:
Journal of Proteome Research More from this journal
Volume:
21
Issue:
4
Pages:
849-864
Publication date:
2022-03-08
Acceptance date:
2022-02-04
DOI:
EISSN:
1535-3907
ISSN:
1535-3893
Language:
English
Keywords:
Pubs id:
1241825
Local pid:
pubs:1241825
Deposit date:
2022-03-01

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