| Added by | jacques.colinge |
|---|---|
| Group name | EquipeJC |
| Item Type | Journal Article |
| Title | Enabling population protein dynamics through Bayesian modeling |
| Creator | Lehmann et al. |
| Author | Sylvain Lehmann |
| Author | Jérôme Vialaret |
| Author | Audrey Gabelle |
| Author | Luc Bauchet |
| Author | Jean-Philippe Villemin |
| Author | Christophe Hirtz |
| Author | Jacques Colinge |
| 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. |
| Publication | Bioinformatics (Oxford, England) |
| Volume | 40 |
| Issue | 8 |
| Pages | btae484 |
| Date | 2024-08-02 |
| Journal Abbr | Bioinformatics |
| Language | eng |
| DOI | 10.1093/bioinformatics/btae484 |
| ISSN | 1367-4811 |
| Library Catalog | PubMed |
| Extra | PMID: 39078204 PMCID: PMC11335370 |
| Tags | anr, Bayes Theorem, Computational Biology, corresponding, Humans, last, original, postdoc, Proteins, top |
| Date Added | 2024/09/08 - 16:55:42 |
| Date Modified | 2024/09/08 - 17:03:19 |
| Notes and Attachments | PubMed entry (Attachment) |
Institut de Recherche en Cancérologie de
