GRNIX: A Graph Neural Network Framework for Explainable Gene Regulatory Network Inference in Autoimmune Diseases Using XAI

Autoimmune diseases result from dysregulated immune mechanisms influenced by complex gene regulatory net-works (GRNs). Deciphering these networks has significant implications for understanding disease mechanisms, predicting disease progression, and identifying novel therapeutic targets. Traditional GRN inference techniques rely on statistical correlations or deterministic models, which are limited in capturing nonlinear interactions and often fail to provide interpretable outputs. Machine learning (ML)-based approaches, while more powerful, typically function as black-box systems, impeding their adoption in clinical settings. To bridge this gap, we introduce GRNIX, a GRN inference framework designed to balance predictive accuracy with explainability. The framework integrates multi-omics data, incorporates biological and structural priors, and applies explainable artificial intelligence (XAI) techniques to enhance interpretability..