Physiology-informed regularisation enables training of universal differential equation systems for biological applications.

ABSTRACT: Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. On the other hand, data-driven approaches such as machine learning models require large volumes of data to produce generalisable models. The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than neural networks alone. Nevertheless, estimating

SUBMITTER: de Rooij M 

PROVIDER: S-EPMC11771921 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature