2 citations · 3 across the 4 of their papers we have counts for
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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…
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…
Modeling cell differentiation in neuroblastoma: insights into development, malignancy, and treatment relapse
Simon F. Martina-Perez, Luke A. Heirene, Jennifer C. Kasemeier +2
Neuroblastoma is a paediatric extracranial solid cancer that arises from the developing sympathetic nervous system and is characterised by an abnormal distribution of cell types in…
Optimal control in combination therapy for heterogeneous cell populations with drug synergies
Simon F. Martina-Perez, Samuel W. S. Johnson, Rebecca M. Crossley +3
Cell heterogeneity plays an important role in patient responses to drug treatments. In many cancers, it is associated with poor treatment outcomes. Many modern drug combination the…