A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks

Ioan Gabriel Bucur, Tom Bussel, Tom Claassen, Tom Heskes
Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:37-48, 2018.

Abstract

Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence tests on the covariance structure meant to progressively reduce the space of possible causal models. We propose a novel efficient Bayesian method for discovering the local causal relationships among triplets of (normally distributed) variables. In our approach, we score the patterns in the covariance matrix in one go and we incorporate the available background knowledge in the form of priors over causal structures. Our method is flexible in the sense that it allows for different types of causal structures and assumptions. We apply the approach to the task of inferring gene regulatory networks by learning regulatory relationships between gene expression levels. We show that our algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships. We demonstrate the stability and efficacy of our method both on simulated data and on real-world data from an experiment on yeast.

Cite this Paper

BibTeX
@InProceedings{pmlr-v72-bucur18a, title = {A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks}, author = {Bucur, Ioan Gabriel and van Bussel, Tom and Claassen, Tom and Heskes, Tom}, booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, pages = {37--48}, year = {2018}, editor = {Kratochvíl, Václav and Studený, Milan}, volume = {72}, series = {Proceedings of Machine Learning Research}, month = {11--14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v72/bucur18a/bucur18a.pdf}, url = {https://proceedings.mlr.press/v72/bucur18a.html}, abstract = {Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence tests on the covariance structure meant to progressively reduce the space of possible causal models. We propose a novel efficient Bayesian method for discovering the local causal relationships among triplets of (normally distributed) variables. In our approach, we score the patterns in the covariance matrix in one go and we incorporate the available background knowledge in the form of priors over causal structures. Our method is flexible in the sense that it allows for different types of causal structures and assumptions. We apply the approach to the task of inferring gene regulatory networks by learning regulatory relationships between gene expression levels. We show that our algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships. We demonstrate the stability and efficacy of our method both on simulated data and on real-world data from an experiment on yeast.} }
Endnote
%0 Conference Paper %T A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks %A Ioan Gabriel Bucur %A Tom Bussel %A Tom Claassen %A Tom Heskes %B Proceedings of the Ninth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2018 %E Václav Kratochvíl %E Milan Studený %F pmlr-v72-bucur18a %I PMLR %P 37--48 %U https://proceedings.mlr.press/v72/bucur18a.html %V 72 %X Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence tests on the covariance structure meant to progressively reduce the space of possible causal models. We propose a novel efficient Bayesian method for discovering the local causal relationships among triplets of (normally distributed) variables. In our approach, we score the patterns in the covariance matrix in one go and we incorporate the available background knowledge in the form of priors over causal structures. Our method is flexible in the sense that it allows for different types of causal structures and assumptions. We apply the approach to the task of inferring gene regulatory networks by learning regulatory relationships between gene expression levels. We show that our algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships. We demonstrate the stability and efficacy of our method both on simulated data and on real-world data from an experiment on yeast.
APA
Bucur, I.G., Bussel, T., Claassen, T. & Heskes, T.. (2018). A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. Proceedings of the Ninth International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 72:37-48 Available from https://proceedings.mlr.press/v72/bucur18a.html.

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