In translational research, teams need to move from an omics signature to a defensible biological story that can guide target selection and downstream experiments.
This requires both literature evidence analysis and multi-layer mechanistic validation: parsing full texts, assessing evidence per gene, integrating disease context, pathway/network activity, tissue/cell-type specificity, and testing whether the observed pattern is mechanistically coherent rather than a statistical artifact.
Manual review is slow, inconsistent, and hard to reproduce across programs. Biolevate standardizes the workflow with passage-level traceability, per-gene and signature-level scoring, and KPI-driven coherence analysis to support faster, more confident prioritization.
Per-gene evidence dossiers
Build structured evidence dossiers for each gene or target by extracting source-linked findings from articles, internal reports, omics analyses, and disease-context evidence.
Reproducible mechanistic scoring
Apply consistent criteria across genes, pathways, articles, and disease contexts to produce a mechanistic coherence score and reduce reviewer-to-reviewer variability.
Defensible target prioritization
Prioritize targets with source-linked rationales that connect genes, mechanisms, pathways, biology, and disease context into a reviewable decision trail.
How it works
1
Ingest signature inputs and scope — provide signature data, disease context, up/down regulation hypotheses, and relevant articles or retrieval criteria to frame the analysis.
2
Parse, index, and extract evidence — Biolevate parses public and proprietary full-text literature, extracts passage-level evidence per gene, and makes every justification searchable and traceable.
3
Run per-gene and mechanistic coherence analysis — compute evidence scores (mechanistic support, causal evidence, consensus) and coherence KPIs across pathways, networks, regulation direction, tissue/cell-type specificity, and fit-to-disease.
4
Generate traceable deliverables — produce per-gene evidence dossiers and a signature-level mechanistic report with standardized KPIs, source-linked claims, and a reusable run record for review and cross-project comparison.
Source-linked biological rationale: every gene-level claim, evidence score, and prioritization decision is linked to supporting passages, datasets, and reviewer logic.
Multi-layer signature analysis: expert teams can assess pathway, network, regulation direction, tissue/cell-type specificity, and fit-to-disease as supporting layers beneath the main evidence review.