WiseR: An end-to-end structure learning and deployment framework for causal graphical models
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Abstract (arXiv)
Structure learning offers an expressive, versatile and explainable approach to causal and mechanistic modeling of complex biological data. We present wiseR, an open source application for learning, evaluating and deploying robust causal graphical models using graph neural networks and Bayesian networks. We demonstrate the utility of this application through application on for biomarker discovery in a COVID-19 clinical dataset.
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References · 5
- 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
- 2016Structure Learning in Graphical Modelingcited by 66
- 2014Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Packagecited by 19
- 2020Learning Explainable Interventions to Mitigate HIV Transmission in Sex Workers Across Five States in Indiacited by 2
- 2018Learning to Address Health Inequality in the United States with a Bayesian Decision Networkcited by 1
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