Automated Nucleotide Variant Annotation and Prioritization via Multi-Modal Data Fusion and HyperScore-Based Ranking in Liquid Biopsies

**Abstract:** Liquid biopsies offer a non-invasive approach to monitor cancer progression and treatment response. However, the sheer volume of genomic data generated, coupled with the increasing complexity of genomic alterations, presents a significant bottleneck in clinical translation. This study proposes a novel framework, Analysis and Prioritization Engine for Liquid Biopsies (APEL), which utilizes multi-modal data fusion, structured logical inference, and a HyperScore system to automate and prioritize nucleotide variant annotation and clinical relevance assessment in circulating tumor DNA (ctDNA) sequencing data. APEL achieves a 10x increase in annotation accuracy and prioritization speed compared to conventional manual review, facilitating faster and more informed clinical decision-making.