
Truveta
Saving lives with data by providing regulatory-grade safety and effectiveness data.

Accelerating drug discovery and precision medicine through federated learning and multi-modal AI.

Owkin is a leader in the AI-biotech sector, specialized in bridging the gap between medical research and clinical application through its proprietary Federated Learning (FL) architecture. By utilizing the open-source Substra framework, Owkin enables pharmaceutical companies and academic researchers to train advanced machine learning models on decentralized, high-quality patient data without ever moving the data from its host institution. This ensures strict compliance with global privacy regulations such as GDPR and HIPAA while overcoming the data silo problem inherent in healthcare. In 2026, Owkin's market position is defined by its massive 'MOSAIC' network—the world's largest spatial transcriptomics dataset—and its suite of AI-driven diagnostic tools like MSIntuit. The platform's technical core excels in multi-modal data integration, combining digital pathology, genomics, and longitudinal clinical records to predict patient outcomes, identify novel drug targets, and optimize clinical trial cohorts for higher success rates. Its architecture is designed for extreme interoperability, connecting global hospitals with life science R&D teams via secure, auditable, and traceable AI workflows.
Owkin is a leader in the AI-biotech sector, specialized in bridging the gap between medical research and clinical application through its proprietary Federated Learning (FL) architecture.
Explore all tools that specialize in discover biomarkers. This domain focus ensures Owkin delivers optimized results for this specific requirement.
Explore all tools that specialize in biomarker discovery. This domain focus ensures Owkin delivers optimized results for this specific requirement.
A secure, decentralized orchestration layer that allows model training on local servers without data transfer.
Advanced neural architectures that process histology slides and genomic sequences simultaneously.
AI-driven screening for Microsatellite Instability (MSI) from digital pathology slides.
Access to massive spatial transcriptomics datasets across 7 cancer types.
Methods to link disparate patient datasets using anonymized tokens.
Visualization tools that highlight the biological features driving AI predictions.
AI model specifically tuned to predict survival in DLBCL patients treated with R-CHOP therapy.
Strategic partnership alignment and therapy area selection.
Deployment of Substra nodes within the partner's secure infrastructure.
Data curation and standardization to the OMOP Common Data Model.
Implementation of privacy-preserving federated learning protocols.
Model training across decentralized hospital datasets.
Validation against independent multi-centric cohorts.
Integration with digital pathology LIS or pharmaceutical R&D pipelines.
Regulatory submission support for AI-based diagnostics (CE-IVD/FDA).
Deployment of explainable AI interfaces for clinician review.
Continuous monitoring and model retraining via real-world data loops.
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"Highly regarded in the biotech industry for its rigorous scientific approach and unique privacy-preserving capabilities. Partners praise its ability to unlock 'un-sharable' medical data."
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