Causal Inference, Biomarker Discovery, Graph Neural Network, Feature Selection

TL;DR

This paper introduces Causal-GNN, a novel method combining causal inference and graph neural networks to improve biomarker discovery by ensuring stability and biological interpretability across diverse datasets.

Contribution

It develops a causal graph neural network that integrates causal effect estimation with GNN-based propensity scoring for stable, interpretable biomarker identification.

Findings

Achieves high predictive accuracy across multiple datasets and classifiers.

Identifies more stable biomarkers than traditional methods.

Provides a robust and interpretable tool for biomarker discovery.

Abstract

Biomarker discovery from high-throughput transcriptomic data is crucial for advancing precision medicine. However, existing methods often neglect gene-gene regulatory relationships and lack stability across datasets, leading to conflation of spurious correlations with genuine causal effects. To address these issues, we develop a causal graph neural network (Causal-GNN) method that integrates causal inference with multi-layer graph neural networks (GNNs). The key innovation is the incorporation of causal effect estimation for identifying stable biomarkers, coupled with a GNN-based propensity scoring mechanism that leverages cross-gene regulatory networks. Experimental results demonstrate that our method achieves consistently high predictive accuracy across four distinct datasets and four independent classifiers. Moreover, it enables the identification of more stable biomarkers compared to traditional methods. Our work provides a robust, efficient, and biologically interpretable tool for biomarker discovery, demonstrating strong potential for broad application across medical disciplines.