Deciphering tuberculosis pathway mechanisms via graph neural networks and multimodal deep learning: a comprehensive AI-driven framework for precision medicine.

Background: Tuberculosis (TB) remains a global health crisis, with complex molecular mechanisms that are not fully understood. Traditional pathway analysis methods fail to capture the intricate non-linear relationships within biological networks. Methods: We developed a novel artificial intelligence framework integrating graph neural networks (GNNs), transformer architectures, and multimodal deep learning to decipher TB pathway mechanisms. Our approach constructs a comprehensive pathway-gene interaction network from three critical pathways (Tuberculosis hsa05152, Antigen processing hsa04612, and NF-κB signaling hsa04064) and employs three interconnected models: (1) a Graph Convolutional Network for learning pathway-gene relationships, (2) a Transformer encoder for pathway activity prediction, and (3) a multimodal fusion model with attention mechanisms integrating transcriptomic, pathway, and clinical data. The framework was trained and validated on 467 clinical samples and 529 transcriptomic samples from five GEO datasets. Results: The Transformer model achieved strong performance in pathway activity prediction ( Conclusion: This study represents the first comprehensive application of GNNs and multimodal deep learning to TB pathway analysis. Our framework provides novel insights into TB pathogenesis, identifies potential therapeutic targets, and demonstrates the power of AI-driven approaches for understanding complex disease mechanisms. The interpretability of our models through attention mechanisms enables translation of computational findings into actionable biological insights, with significant implications for precision medicine and personalized TB treatment strategies.