Accelerated Fibroblast Differentiation Prediction via Multi-Modal Graph Neural Networks in Idiopathic Pulmonary Fibrosis (IPF)

**Abstract:** Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung disease characterized by excessive fibroblast differentiation and extracellular matrix deposition. Current diagnostic and prognostic methods lack sensitivity and specificity for early intervention. This paper proposes a novel framework leveraging multi-modal graph neural networks (MMGNNs) to predict fibroblast differentiation trajectories from patient-derived data, accelerating research and enabling personalized therapeutic strategies. Our approach integrates transcriptomic, proteomic, and imaging data—specifically, high-resolution computed tomography (HRCT) scans—into a unified graph representation. This allows for a holistic view of IPF pathogenesis and provides significantly improved prediction accuracy compared to single-modality models. The system aims for immediate commercializability through integration into clinical diagnostic pipelines and pharmaceutical drug discovery platforms, potentially revolutionizing IPF management.