The efficacy and potential toxicity of drug treatments depends on the drug concentration at
its site of action, intricately linked to its distribution within diverse organelles of mammalian …

Parameter estimation is one of the central challenges in computational biology. In this paper,
we present an approach to estimate model parameters and assess their identifiability in …

The advent of machine learning has led to innovative approaches in dealing with clinical
data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models …

Introduction Isoniazid is a first-line antituberculosis agent with high variability, which would
profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of …

Serum creatinine in neonates follows complex dynamics due to maturation processes, most
pronounced in the first few weeks of life. The development of a mechanism‐based model …

Introduction: This study explores using Neural Ordinary Differential Equations (NODEs) to
analyze hormone dynamics in the hypothalamicpituitary-adrenal (HPA) axis during Trier …

Neural ordinary differential equations (NODEs) are an emerging machine learning (ML)
method to model pharmacometric (PMX) data. Combining mechanism‐based components …

Conventional pharmacokinetic (PK) models contain several useful inductive biases guiding
model convergence to more realistic predictions of drug concentrations. Implementing …

This work focusses on extending the deep compartment model (DCM) framework to the
estimation of mixed-effects. By introducing random effects, model predictions can be …

Learning models for dynamical systems in continuous time is significant for understanding
complex phenomena and making accurate predictions. This study presents a novel …