Enabling population protein dynamics through Bayesian modeling.
Bioinformatics
; 40(8)2024 Aug 02.
Article
en En
| MEDLINE
| ID: mdl-39078204
ABSTRACT
MOTIVATION The knowledge of protein dynamics, or turnover, in patients provides invaluable information related to certain diseases, drug efficacy, or biological processes. A great corpus of experimental and computational methods has been developed, including by us, in the case of human patients followed in vivo. Moving one step further, we propose a novel modeling approach to capture population protein dynamics using Bayesian methods. RESULTS:
Using two datasets, we demonstrate that models inspired by population pharmacokinetics can accurately capture protein turnover within a cohort and account for inter-individual variability. Such models pave the way for comparative studies searching for altered dynamics or biomarkers in diseases. AVAILABILITY AND IMPLEMENTATION R code and preprocessed data are available from zenodo.org. Raw data are available from panoramaweb.org.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas
/
Teorema de Bayes
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Francia