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: | |
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: | |
Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |