Mechanistic validation is a critical step to turn an omics signature into a credible biological story.
Teams must integrate multi-layer evidence (signature data, disease context, up/down regulation) and test whether the signature is coherent with known pathways, networks, and tissue/cell-type specificity.
Doing this rigorously and repeatably is time-consuming and hard to standardize across programs - yet it is key to prioritize targets and de-risk downstream development.
3× faster mechanistic validation
Accelerate signature interpretation by automating evidence integration, pathway/network analysis, and KPI computation in a single repeatable workflow.
More confident prioritization
Quantify mechanistic coherence and fit-to-disease to rank signatures and reduce false leads before costly downstream experiments.
Standardized, comparable KPIs
Use consistent biology/network/context metrics across teams to improve reproducibility and make cross-project comparisons defensible.
How it works
1
Ingest multi-layer inputs — upload signature data, define the disease context, and specify up/down regulation hypotheses to frame the analysis.
2
Run the Biological Core Engine — perform regulation system analysis, compare against known hubs, and assess tissue/cell-type specificity to interpret signature elements.
3
Quantify mechanistic coherence KPIs — compute pathway mapping (biology coherence), interaction structure (network regulation), and independent context fit-to-disease to measure functional integrity.
4
Deliver a traceable, repeatable analysis — generate a structured report with per-gene/per-pathway evidence and coherence results, enabling consistent review and reuse across projects.
Multi-layer evidence integration: combines signature data with disease context and regulation direction to produce a coherent mechanistic narrative.
KPI-driven mechanistic scoring: standardized KPIs (pathways, networks, context fit) to compare signatures objectively across programs.