WiseR: An end-to-end structure learning and deployment framework for causal graphical models

TL;DR

WiseR is an open-source framework that combines graph neural networks and Bayesian networks to learn, evaluate, and deploy causal graphical models, demonstrated on COVID-19 biomarker discovery.

Contribution

This paper introduces wiseR, a comprehensive end-to-end tool for structure learning and deployment of causal models in biological data analysis.

Findings

Effective in biomarker discovery for COVID-19

Integrates graph neural networks with Bayesian networks

Provides a versatile platform for causal modeling

Abstract

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.