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@article{200868,
        author = {Sure Tharun Kumar and M. Padmavathamma},
        title = {Genomics-Driven Personalized Treatment Recommendation System for Parkinson’s Disease using Graph Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3104-3110},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200868},
        abstract = {Parkinson’s Disease (PD) is a complex neurode- generative disorder where treatment efficacy is significantly influenced by individual genomic variations. Traditional clinical workflows often rely on trial-and-error methodologies, failing to account for high-dimensional drug-gene interactions and polygenic risks. This research proposes a precision medicine framework that integrates multi-modal genomic and clinical data through Graph Neural Networks (GNNs) and BioBERT- based clinical analysis .The architecture leverages a Graph Convolutional Network (GCN) to model drug-gene interaction graphs, alongside a BioBERT-powered engine for extracting sentiment and urgency from unstructured patient notes. To foster digital trust, the framework incorporates visual and mathematical interpretability layers that explain the underlying rationale for personalized treatment recommendations. Our experimental results demonstrate that the synthesis of genomic feature extraction and graph-based modeling achieves high predictive accuracy and provides explainable insights for clinicians, effectively bridging the gap between genomic data and patient-centric care.},
        keywords = {Parkinson’s Disease, Genomics, Graph Neural Networks (GNN), Explainable AI (XAI), BioBERT, Precision Medicine, Multi-modal Data Fusion, Deep Learning, Clinical Decision Support Systems (CDSS).},
        month = {May},
        }