Inferring network structure from interventional time-course experiments
arXiv:1504.07882 · doi:10.1214/15-AOAS806
Abstract
Graphical models are widely used to study biological networks. Interventions on network nodes are an important feature of many experimental designs for the study of biological networks. In this paper we put forward a causal variant of dynamic Bayesian networks (DBNs) for the purpose of modeling time-course data with interventions. The models inherit the simplicity and computational efficiency of DBNs but allow interventional data to be integrated into network inference. We show empirical results, on both simulated and experimental data, that demonstrate the need to appropriately handle interventions when interventions form part of the design.
Published at http://dx.doi.org/10.1214/15-AOAS806 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
References in corpus (5)
- Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
- Learning the Structure of Dynamic Probabilistic Networks
- External Validity: From Do-Calculus to Transportability Across Populations
- Estimating high-dimensional intervention effects from observational data
- Inferring network structure from interventional time-course experiments