Hyperdimensional Phenotype Mapping for Subtype Identification in Autism Spectrum Disorder via Multi-omic Integration

**Abstract:** Autism Spectrum Disorder (ASD) represents a heterogeneous condition with diverse clinical presentations and underlying etiologies. Current diagnostic criteria fail to capture this heterogeneity, hindering personalized therapeutic interventions. This paper proposes a novel computational framework, Hyperdimensional Phenotype Mapping (HPDM), that integrates genomic, proteomic, and clinical datasets via hyperdimensional algebra to identify distinct ASD subtypes with enhanced accuracy and mechanistic insights. HPDM leverages a robust, scalable machine learning pipeline to transform complex multi-omic data into high-dimensional hypervectors, facilitating pattern recognition and subtype delineation. Preliminary results demonstrate the potential for HPDM to stratify ASD patients into statistically distinct groups with identifiable biological signatures, paving the way for targeted therapies and improved diagnostic precision.