24 citations · 25 across the 9 of their papers we have counts for
7 papers · 1 filter
An efficient and flexible generalization of transit compartment models for delayed biological processes
Giulia Belluccini, Christiaan H. van Dorp, Linjie Huang +3
Transit compartment models are widely used in pharmacokinetics/pharmacodynamics to represent delayed biological processes such as absorption, maturation, cellular proliferation, an…
Frequentist and Bayesian population pharmacometrics: similarities, differences, and practical implications
Jodie A. Cochrane, Haini Wen, Liang Yang +2
Although the frequentist versus Bayesian debate has a long-standing history, and both are well-established approaches for inference, they are often considered separately in pharmac…
Hyperparameter selection for equation learning with biologically-informed neural networks
William Lavery, Jodie A. Cochrane, John T. Nardini +1
Biologically-informed neural networks (BINNs) have emerged as a flexible subclass of physics-informed neural networks (PINNs) for learning terms in partial differential equations f…
The MCMC convergence graph: a diagnostic for multimodal posteriors
Cici Chen Gu, Eszter Lakatos, Sara Hamis
Multimodality is common in many scientific and engineering applications. However, standard Markov chain Monte Carlo (MCMC) convergence diagnostics often penalise multimodality in p…
Adaptive therapy under parametric, structural, and measurement uncertainty
Alexander P Browning, Rebecca M Crossley, Ryan J Murphy +2
Adaptive therapy has emerged as a promising treatment strategy that exploits within-tumour competition to delay disease progression. Implementation, however, typically relies on in…
Physics-Informed Neural Networks for Biological Reaction-Diffusion Systems
William Lavery, Jodie A. Cochrane, Christian Olesen +3
Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-informed neural networks (BINNs)…
A practical introduction to ODE modelling in Stan for biological systems
Sara Hamis, John Forslund, Cici Chen Gu +1
Integrating dynamical systems models with time series data is a central part of contemporary mathematical biology. With the rich variety of available models and data, numerous meth…