CFE 2019: Start Registration
View Submission - CMStatistics
Title: Non-homogeneous dynamic Bayesian networks with Bayesian regularization for gene regulatory network inference Authors:  Frank Dondelinger - Lancaster University (United Kingdom)
Sophie Lebre - IMAG (France) [presenting]
Dirk Husmeier - Biomathematics and Statistics Scotland, Edinburgh (UK)
Abstract: The proper functioning of any living cell relies on complex networks of gene regulation. These regulatory interactions are not static, but respond to changes in the environment and evolve during the life cycle of an organism. A challenging objective in computational systems biology is to infer these time-varying gene regulatory networks from typically short time series of transcriptional profiles. While homogeneous models, like conventional dynamic Bayesian networks, lack the flexibility to succeed in this task, fully flexible models suffer from inflated inference uncertainty due to the limited amount of available data. We explore here a semi-flexible model based on a piecewise homogeneous dynamic Bayesian network regularized by gene-specific inter-segment information sharing. We consider different choices of prior distribution and information coupling, and evaluate their performance on synthetic data and gene expression time series.