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  "Title": "Bayesian Hierarchical Modeling for Label-Free Proteomics",
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  "Authors@R": "person(\"Philip\", \"Berg\", , \"pberg@live.se\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-3772-6185\"))",
  "Description": "Statistical decision in proteomics data using a\nhierarchical Bayesian model. There are two regression models\nfor describing the mean-variance trend, a gamma regression or a\nlatent gamma mixture regression. The regression model is then\nused as an Empirical Bayes estimator for the prior on the\nvariance in a peptide. Further, it assumes that each\nmeasurement has an uncertainty (increased variance) associated\nwith it that is also inferred. Finally, it tries to estimate\nthe posterior distribution (by Hamiltonian Monte Carlo) for the\ndifferences in means for each peptide in the data. Once the\nposterior is inferred, it integrates the tails to estimate the\nprobability of error from which a statistical decision can be\nmade. See Berg and Popescu for details\n(<doi:10.1016/j.mcpro.2023.100658>).",
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        "baldur"
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