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

Shubham Maheshwari; Khushbu Pahwa; Tavpritesh Sethi

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arXiv · 2108.07046

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

Shubham Maheshwari·Khushbu Pahwa·Tavpritesh Sethi

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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.

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Shubham Maheshwari, Khushbu Pahwa, Tavpritesh Sethi. 2021-08-16. WiseR: An end-to-end structure learning and deployment framework for causal graphical models. https://arxiv.org/abs/2108.07046

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