MMGT-PD: A Multi-Modal Graph Transformer for Parkinson’s Disease Stage Classification Using Clinical Omics and Whole Blood RNA Sequencing Data

The assessment of both motor and non-motor functions in Parkinson's disease PD plays a crucial role in disease diagnosis and early intervention. In recent years, multi-modal deep learning methods have demonstrated excellent performance in identifying disease subtypes. However, previous studies have primarily focused on clinical and transcriptomic data, neglecting the information on gene associations. This paper proposes a multi-modal graph Transformer model named MMGT-PD, which integrates whole blood RNA sequencing data, gene co-expression networks, and Clinical omics data, combining modality-specific and consensus information to significantly enhance the accuracy of Parkinson's disease diagnosis. The model constructs a gene co-expression network using RNA sequencing data and designs an RNA sequencing encoder that combines Graph Attention Network GAT and Kolmogorov-Arnold Network KAN to extract RNA-specific representations. Additionally, the model introduces the Genegraph-Clinic Fusion GCFusion module to enhance the integration of multi-modal data by extracting shared information through inter-modal interactions. This paper conducts extensive comparative experiments on two well-known Parkinson's disease datasets, and the results show that the MMGT-PD method outperforms baseline models.