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Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.

Integrating the sinh-arcsinh distribution into a distributional regression framework allows us to produce precise, local estimates of sexual age-mixing, facilitating more accurate modelling of sexually transmitted disease dynamics.

Compiling public datasets into a single, centralised repository and linking directly to analytical software completely transforms the scale and scope of causal inference across the phenome.

A new mathematical method has been developed, implemented and validated for the analysis of time-lapse confocal laser scanning microscopy images to characterize the swimming behavior of bacterial swimmers moving in the exogenous matrix of pathogenic biofilms.

A new methodology and protocol use automated brain imaging and task training technology to probe links between behavior and mouse cortical circuits.

Experimental determination of residue contacts from mutational data allows model discrimination and identification of in vivo functional conformations of proteins.

A flexible and accurate methodology to fit reduced compartmental models to dendritic morphologies shows that models with very few compartments can capture many dendritic computations.