Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices

Abstract:

Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular modelling tool for learning cellular networks from time series data. In systems biology, time series are often measured under different experimental conditions, and not rarely only some network interaction parameters depend on the condition while the other parameters stay constant across conditions. For this situation, we propose a new partially NH-DBN, based on Bayesian hierarchical regression models with partitioned design matrices. With regard to our main application to semi-quantitative (immunoblot) timecourse data from mammalian target of rapamycin complex 1 (mTORC1) signalling, we also propose a Gaussian process-based method to solve the problem of non-equidistant time series measurements.

SEEK ID: https://fairdomhub.org/publications/639

DOI: 10.1093/bioinformatics/bty917

Projects: MESI-STRAT

Publication type: Journal

Journal: Bioinformatics

Editors: Jonathan Wren

Citation: Bioinformatics 35(12):2108-2117

Date Published: 1st Jun 2019

Registered Mode: by DOI

Authors: Mahdi Shafiee Kamalabad, Alexander Martin Heberle, Kathrin Thedieck, Marco Grzegorczyk

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Shafiee Kamalabad, M., Heberle, A. M., Thedieck, K., & Grzegorczyk, M. (2018). Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices. In J. Wren (Ed.), Bioinformatics (Vol. 35, Issue 12, pp. 2108–2117). Oxford University Press (OUP). https://doi.org/10.1093/bioinformatics/bty917
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Created: 11th Jan 2022 at 12:51

Last updated: 11th Jan 2022 at 12:52

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