Uncertainty Quantification in Dynamic Models of Biological Systems Using Conformal Prediction
Uncertainty Quantification in Dynamic Models of Biological Systems Using Conformal Prediction
Software and supplementary information for the paper: Portela, A., J.R. Banga, M. Matabuena (2025) Conformal Prediction for Uncertainty Quantification in Dynamic Biological Systems. PLOS Computational Biology 21(5): e1013098. https://doi.org/10.1371/journal.pcbi.1013098 Previous version: Portela, Alberto, Julio R. Banga and Marcos Matabuena (2024) Conformal Prediction in Dynamic Biological Systems. arXiv:2409.02644. https://arxiv.org/abs/2409.02644 Funding: JRB acknowledges support from grant PID2020-117271RB-C22 (BIODYNAMICS) funded by MCIN/AEI/10.13039/501100011033, from grant PID2023-146275NB-C22 (DYNAMO-bio) funded by MICIU/AEI/ 10.13039/501100011033, and from grant CSIC PIE 202470E108 (LARGO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- Harvard University United States
- Misión Biológica de Galicia Spain
- Spanish National Research Council Spain
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!