WiseR: An end-to-end structure learning and deployment framework for causal graphical models
This provides a tool for researchers in biology and medicine to build explainable causal models, though it appears incremental as it combines existing methods into a framework.
The authors tackled the problem of learning and deploying causal graphical models for complex biological data by introducing wiseR, an open-source framework that uses graph neural networks and Bayesian networks, and demonstrated its utility for biomarker discovery in a COVID-19 clinical dataset.
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.