Understand Why Your Trial Failed.
De-Risk Your Next One.
Multi-omics AI that reveals why patients didn't respond—and predicts who will respond to your therapy.
Multi-Omics AI That Understands Biology
Traditional approaches analyze single data types in isolation. We integrate orthogonal high-dimensional datasets—microbiome, metabolome, proteome, transcriptome, epigenome—to build models that approximate how human biology actually works.
- Orthogonality Advantage: Independent data sources yield exponential improvements in forecasting accuracy
- Biological Approximation: Models that represent mechanisms, not correlations
- Clinical Actionability: Predictions that inform treatment decisions
Built on Proven Foundations
Our platform is powered by scikit-bio, our DOE-funded open-source platform with thousands of users worldwide.
- Proven, reproducible, scientifically validated
- 1,000,000+ curated samples
- 5+ years of database development
- Validated in Nature journals
Trusted By
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Immunotherapy Response Prediction: A Multi-Omics Approach
Learn how multi-omics biomarkers can dramatically improve patient selection for immunotherapy, reducing Phase 3 trial failure rates and accelerating drug development timelines.
- Why most patients don't respond to immunotherapy
- How multi-omics biomarkers improve patient selection
- Real-world case studies from pharma development
Transform Treatment Prediction
Discuss how our platform can de-risk your clinical development program
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