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.
