Complex systems and Biological physics seminar [before December 2013]

Gaussian Process Models of Gene Expression and Transcriptional Regulation

by Antti Honkela (Aalto University and HIIT)

Europe/Stockholm
122:026

122:026

Description
Biological systems are inherently dynamic and time series data provide great insight to understanding them. Such data are most naturally modelled in continuous-time framework that can be directly applied to data with diverse or uneven sampling. Gaussian processes provide a convenient tool for specifying priors over latent continuous-time functions in such models. I will present models combining Gaussian processes with a differential equation model of gene regulation for predicting target genes of transcription factors. Extending these models with a hierarchical Gaussian process allows modelling diverse experimental setups, such as mixed longitudinal/cross-sectional designs and phylogenetic structure. The methods are applied to modelling gene regulation in Drosophila development and a multi-species Drosophila data set for modelling evolution of gene expression.