Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data.

ABSTRACT:

Motivation

Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties.

Results

We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dy

SUBMITTER: Hopfl S 

PROVIDER: S-EPMC11128094 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature