BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks

Nikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:593-596, 2020.

Abstract

BayesSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks that can scale to tens of thousands of nodes while providing fast and friendly user experience. BayesSuites solves the problems of scalability, extensibility and interpretability that massive networks bring by separating backend calculations from the frontend interface and using specialized learning algorithms for massive networks. We demonstrate the tool by learning and visualizing a genome-wide gene regulatory network from human brain data with 20,708 nodes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v138-bernaola20a, title = {Bayes{S}uites: {A}n {O}pen {W}eb {F}ramework for {V}isualization of {M}assive {B}ayesian {N}etworks}, author = {Bernaola, Nikolas and Michiels, Mario and Bielza, Concha and Larra{\~n}aga, Pedro}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {593--596}, year = {2020}, editor = {Jaeger, Manfred and Nielsen, Thomas Dyhre}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/bernaola20a/bernaola20a.pdf}, url = {https://proceedings.mlr.press/v138/bernaola20a.html}, abstract = {BayesSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks that can scale to tens of thousands of nodes while providing fast and friendly user experience. BayesSuites solves the problems of scalability, extensibility and interpretability that massive networks bring by separating backend calculations from the frontend interface and using specialized learning algorithms for massive networks. We demonstrate the tool by learning and visualizing a genome-wide gene regulatory network from human brain data with 20,708 nodes.} }
Endnote
%0 Conference Paper %T BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks %A Nikolas Bernaola %A Mario Michiels %A Concha Bielza %A Pedro Larrañaga %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-bernaola20a %I PMLR %P 593--596 %U https://proceedings.mlr.press/v138/bernaola20a.html %V 138 %X BayesSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks that can scale to tens of thousands of nodes while providing fast and friendly user experience. BayesSuites solves the problems of scalability, extensibility and interpretability that massive networks bring by separating backend calculations from the frontend interface and using specialized learning algorithms for massive networks. We demonstrate the tool by learning and visualizing a genome-wide gene regulatory network from human brain data with 20,708 nodes.
APA
Bernaola, N., Michiels, M., Bielza, C. & Larrañaga, P.. (2020). BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:593-596 Available from https://proceedings.mlr.press/v138/bernaola20a.html.

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