Challenges and Opportunities for Bayesian Statistics in Proteomics
Author:
Affiliation:
1. Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
2. Structural and Biophysical Sciences, GlaxoSmithKline R&D, Stevenage SG1 2NY, United Kingdom
Funder
GlaxoSmithKline
New College, University of Oxford
Publisher
American Chemical Society (ACS)
Subject
General Chemistry,Biochemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.1c00859
Reference106 articles.
1. Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
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3. A Comprehensive Subcellular Atlas of the Toxoplasma Proteome via hyperLOPIT Provides Spatial Context for Protein Functions
4. Bates, D. Fitting linear mixed-effects models using lme4. arXiv preprint, arXiv:1406.5823, 2014. https://arxiv.org/abs/1406.5823.
5. Objective Bayesian Analysis of Spatially Correlated Data
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