The article presents PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks to prioritize therapeutic targets for Alzheimer's disease (AD). PI4AD recovers clinically validated targets like APP and ESR1, confirming its prioritization efficacy. The framework identifies Ras signaling as a central therapeutic hub, complementing traditional amyloid/tau-focused approaches. Crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1) and drug repurposing opportunities, bridging genetic insights with pathway-level biology.