From raw proteomics to biological understanding.
Teal translates high-dimensional proteomic data into standardized biological models that can be compared across cohorts, studies, and assay platforms.
Measure organ-level biology
Quantify biological aging, resilience, and disease risk across organs and cell types.
Stratify patients & discover mechanisms
Identify molecular subpopulations and uncover pathways driving disease and treatment response.
Track biological change over time
Measure longitudinal biological trajectories across interventions, treatments, and exposures.
Generate publication-ready biological insight
Produce interpretable outputs including biomarkers, pathway analyses, and mechanistic signals.
A proprietary biological
intelligence stack.
Teal harmonizes large-scale proteomic datasets into deployable biological models — flowing from raw infrastructure, through a dense modeling engine, into interpretable scientific output.
- UK Biobank
- GNPC
- 100K+ samples
- Olink
- SomaLogic
- Alamar
- Harmonization
- Organ aging models
- Cell aging models
- Multimodal integration
- Causal inference / pQTL
- Biomarker discovery
- Patient stratification
- Longitudinal response modeling
- Mechanistic pathway analysis
A partner, not a vendor.
Platform-agnostic
Models deployed across Olink, SomaLogic, and Alamar — enabling cross-platform comparison no native tool offers.
Cross-study comparability
Trained on 100K+ samples across 20+ cohorts; comparable signals across studies, populations, and disease contexts.
Causal inference built-in
Mendelian randomization and pQTL analytics turn correlations into mechanistic evidence.
Multimodal integration
Connect proteomics with genomics, imaging, clinical endpoints, and lifestyle data in one pipeline.
Scientific credibility
Built on foundational proteomics research from the Wyss-Coray Lab at Stanford.
End-to-end engagement
From study design through final interpretation — tables, figures, and a methods document, not just a data dump.