糖心TV

Gaussian processes provide a convenient and flexible class of non-parametric model for temporal and spatial data. We are applying Gaussian processes in a range of biological applications involving high-throughput time course data, e.g. modelling the elongation dynamics of polymerase, uncovering mRNA production delays, inferring regulatory networks and most recently identifying perturbations and bifurcations from high-throughput expression data. I will provide an overview of Gaussian process inference and describe some of our recent work in modelling gene expression dynamics. Most recently we have been focusing on single-cell data. Using longitudinal data from microscopy experiments we are using stochastic periodic processes to uncover periodicity controlled by negative feedback loops. Using genome-wide single-cell expression data we are uncovering branching processes and uncovering the order with which different genes differentiate through a developmental process.