The Annals of Applied Statistics

Inferring network structure from interventional time-course experiments

Simon E. F. Spencer, Steven M. Hill, and Sach Mukherjee

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 9, Number 1 (2015), 507-524.

Dates
First available in Project Euclid: 28 April 2015

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1430226102

Digital Object Identifier
doi:10.1214/15-AOAS806

Mathematical Reviews number (MathSciNet)
MR3341125

Zentralblatt MATH identifier
06446578

Keywords
Bayesian inference network inference structure learning causal inference dynamic Bayesian network causal Bayesian network

Citation

Spencer, Simon E. F.; Hill, Steven M.; Mukherjee, Sach. Inferring network structure from interventional time-course experiments. Ann. Appl. Stat. 9 (2015), no. 1, 507--524. doi:10.1214/15-AOAS806. https://projecteuclid.org/euclid.aoas/1430226102


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Supplemental materials

  • Supplement to "Inferring network structure from interventional time-course experiments".: Additional technical information about orthogonalization, the experimental procedure and the intervention models, including a toy example. Supplementary figures showing the prior network, the "true" network used for simulations and the posterior signaling networks without interventions.