Ait Ouares, K.; Weerakkody, J. S.

Extracellular vesicle (EV) studies increasingly generate mixed cargo lists that include genes, proteins, miRNAs, biofluids, cell contexts, disease labels, pathways, and interaction networks. The central interpretive challenge is determining which source supports each record and what level of biological claim that source can justify. We developed EVd3x, a source-attributed multi-omic platform that integrates 28 public resources into 17 canonical Apache Parquet analysis tables and converts molecule, disease, or natural-language queries into a reusable analysis state. The same state can be inspected across linked evidence layers for EV cargo, disease aggregation, pathway enrichment, cell context, ligand receptor evidence, miRNA target support, STRING protein protein interactions, and exportable source rows. We evaluated EVd3x using the disease-first query: early onset Alzheimers disease with behavioral disturbance. The query resolved a PSEN1-centered state with 5 seeds, 109 nodes, and 197 edges, and exported 647 EV evidence rows, 4,053 disease rows, 2,204 pathway rows, 3,555 cell-context/communication/ligand receptor rows, and 4,032 bridge rows. EVd3x recovered familial Alzheimer disease type 3, gamma-secretase and Notch context, nervous-system pathway terms, oligodendrocyte to astrocyte communication hypotheses, and PSEN1 bridges in which six queried miRNAs, including hsa-miR-107, target PSEN1 directly. These outputs are reported as separable evidence layers rather than as a composite proof score. A table-backed research assistant fine-tuned from Qwen2.5-1.5B-Instruct with QLoRA routes natural-language requests through deterministic retrieval before optional synthesis. EVd3x supports transparent EV hypothesis generation by preserving source attribution from query to export.