A Personalized Point-of-Care Platform for Discovery and Validation of miRNA Targets Using AI and Edge Computing Supporting Personalized Cancer Therapy

The paradigm of cancer therapy is rapidly shifting towards personalized precision medicine, yet current diagnostic approaches remain constrained by centralized laboratory infrastructure, creating critical delays between sample collection and therapeutic intervention. To address this limitation, we present GTMIT, a novel point-of-care (POC) platform integrating artificial intelligence (AI) and edge computing for real-time discovery and validation of microRNA (miRNA) targets directly at the patient's bedside. Unlike traditional laboratory-centric models, GTMIT with edge computing operates on local hardware resources (e.g., portable sequencers and mobile devices), enabling POC decision-making without reliance on the cloud. Our framework combines three key innovations: 1) A Transformer-GNN hybrid architecture with Power Normalization for robust miRNA-mRNA interaction prediction; 2) SNP-adaptive Gapped Pattern Graph Convolutional Networks (GP-GCN) accounting for patient-specific genetic variations; and 3) Edge therapeutic optimization incorporating regional cancer prevalence patterns and resource constraints. We evaluate our proposed platform on several clinical datasets. GTMIT demonstrates excellent performance on a range of metrics, achieving 94% AUC, 87% precision, and 79% recall on benchmark datasets.GTMIT demonstrates excellent performance on a range of metrics, achieving 94% AUC, 87% precision, and 79% recall on benchmark datasets. By bridging molecular diagnostics with immediate intervention at the POC, GTMIT reduces time-to-treatment from days to minutes, particularly benefiting resource-limited settings.