Scalable Omic Data Partnerships to Accelerate Target Discovery
06 May 2026
Target Identification
- Discuss the advantages of collaborative data models, including access to greater scale, diversity, and multimodal depth than any single organization can generate alone.
- Learn how population-scale genomics, proteomics, and single-cell CRISPR data are improving target confidence, shortening discovery timelines, and de-risking early programs.
- Explore how ancestrally and clinically diverse datasets uncover biology missed in Eurocentric or narrowly phenotyped cohorts
- Discuss the importance of comprehensive, actionable data as the bases of training machine learning models
- Understand practical next steps for exploring partnerships, from disease-focused datasets to integrated analytics and long-term discovery platforms.
