8 papers
A likelihood-based framework for simultaneously learning both noise and growth dynamics using biologically-informed neural networks
Rebecca M. Crossley, Ruth E. Baker
In recent years, neural ordinary differential equation frameworks such as Biologically-Informed Neural Networks (BINNs) have shown promise for learning mechanistic laws from sparse…
Quantifying the effect of phenotype on clustering behaviour in melanoma: from monoculture to co-culture
Nathan Schofield, Richard White, Ruth Baker +1
Melanoma is an aggressive form of skin cancer. Survival rates are excellent if it is detected early but fall markedly if it metastasises. A key step in early tumour progression is…
Learning functional components of PDEs from data using neural networks
Torkel E. Loman, Yurij Salmaniw, Antonio Leon Villares +2
Partial differential equations often contain unknown functions that are difficult or impossible to measure directly, hampering our ability to derive predictions from the model. Wor…
Modelling collective cell migration in a data-rich age: challenges and opportunities for data-driven modelling
Ruth E. Baker, Rebecca M. Crossley, Carles Falco +1
Mathematical modelling has a long history in the context of collective cell migration, with applications throughout development, disease and regenerative medicine. The aim of model…
A nonlocal-to-local approach to aggregation-diffusion equations
Carles Falcó, Ruth E. Baker, José A. Carrillo
Over the past decades, nonlocal models have been widely used to describe aggregation phenomena in biology, physics, engineering, and the social sciences. These are often derived as…
Optimal experimental design for parameter estimation in the presence of observation noise
Jie Qi, Ruth E. Baker
Using mathematical models to assist in the interpretation of experiments is becoming increasingly important in research across applied mathematics, and in particular in biology and…