EVd3x
EVd3x is source-attributed infrastructure for interpreting extracellular-vesicle evidence and developing future multi-omic models.
Build the evidence layer before training the model.
EVd3x turns a long EV molecular list into a reviewable evidence packet: reported cargo support, disease context, pathways, cell context, regulatory relationships, interaction records, and the experiment needed to move a candidate forward. That source-linked structure is the foundation for future EV-specific machine learning.
A research workspace built for inspectable inference
The current release is designed to help researchers preserve the route from a reported molecular observation to its evidence, context, competing explanations, and verification requirements. It is a faster way to review a complex evidence landscape without hiding the uncertainty that matters.
A source-attributed evidence graph
EVd3x carries a fixed molecular packet through reported EV-cargo records, disease context, pathways, cell context, regulatory relationships, and protein interactions. Each result remains linked to the underlying source rows, filters, and exportable analysis state.
A structured foundation for model development
The platform supports source-linked query routing and table-grounded synthesis across recurring EV research questions. The retrieved evidence records remain visible alongside each research summary, establishing the training foundation for future EV-specific models.
A reviewable output, not a black box
The platform is designed to identify candidate molecular themes, competing interpretations, and the verification step needed next. It treats disease annotations, pathway enrichment, cell-context scores, and interaction records as distinct evidence layers rather than a single biological verdict.
EVd3x as a source-attributed multi-omic platform
Ait Ouares, K., and Weerakkody, J. S. EVd3x: a source-attributed multi-omic platform for mapping extracellular vesicle cargo evidence.bioRxiv, 2026. DOI: 10.64898/2026.05.06.723262.
Train models on evidence first, then on cohorts.
The manuscript lays out a deliberate sequence: turn EV literature into an expert-curated claim–evidence graph, train evidence-aware models, and then connect analytically controlled EV multi-omics to longitudinal clinical outcomes. The next models will learn from curated EV claim–evidence units and cohort data so that their biological reasoning remains traceable.
Curate claim–evidence units
Build expert-reviewed records that specify the strongest supported EV claim, its source publication, preparation and characterization context, assay, biological setting, and missing prerequisites. Contradictory and context-mismatched records become essential training examples rather than noise to hide.
Train EV-specific evidence models
Use the curated graph to train models that classify supported claim boundaries, identify independent versus repeated evidence, surface missing verification steps, and explain why a candidate should advance, remain uncertain, or be rejected.
Link models to longitudinal multi-omic cohorts
Connect analytically controlled EV miRNA, mRNA, protein, lipid, imaging, and clinical data to prespecified outcomes such as progression or treatment response. Cohort-aware model development will prioritize interpretable features, leakage control, and locked external evaluation.
Validate a defined clinical use
Only after the target population, specimen, outcome, threshold, and independent performance are established can a model be evaluated for a diagnostic, prognostic, monitoring, or predictive context of use.
How EVd3x will support EV multi-omic biomarker models
In the clinical research program, model training begins with a defined population and outcome, a locked assay and preprocessing plan, and biological context carried alongside the molecular features. This makes the model useful for progression and treatment-response research while preserving a direct route back to the relevant cells, pathways, and experimental tests.