Automated Prediction of Neoantigen Presentation Efficiency via Integrated Multi-Modal Data Analysis and HyperScore Validation in Dendritic Cell Development

**Abstract:** This paper introduces a novel, fully automated computational framework for predicting the efficiency of neoantigen presentation by dendritic cells (DCs) during immunotherapy development. Leveraging advancements in multi-modal data integration, semantic parsing of scientific literature, and robust statistical modeling, our system, termed "ImunoPred," offers a significant improvement over traditional, human-intensive methods for identifying immunogenic targets and optimizing vaccine design. ImunoPred integrates textual, structural, and numerical data sources, employing a hierarchical evaluation pipeline culminating in a 'HyperScore' that quantifies neoantigen presentation potential. This approach is projected to reduce development timelines by 20-30% and improve vaccine efficacy by 15-25% via targeted neoantigen selection.