Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements

Autor:	Eric J. Ma, Arkadij Kummer
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	hierarchical modelling bayesian statistics probabilistic programming high-throughput measurements Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 23, Iss 6, p 727 (2021)
Druh dokumentu:	article
ISSN:	23060727 1099-4300
DOI:	10.3390/e23060727
Popis:	We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/336db3531b984594ae32f4eee4088704 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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