Building Better AI With High-Resolution Functional Genomics Data

Ci Chu at Xaira explores new approaches to generating large-scale, single-cell perturbation datasets that overcome key challenges in throughput, variability and batch effects.

Presented by

Ci Chu, PhD

Vice President, Early Discovery, Xaira

Covered in this Webinar

Learn about the application and limitations of perturbation data generation methods and how to overcome these bottlenecks to enable scalable, high-quality single-cell transcriptomic profiling

Explore the features of FiCS Perturb-seq and the X-Atlas/Orion dataset and how they form a resource for training biological foundation models

Discover how dose-dependent genetic effects, captured through single guide RNA abundance, enhance the predictive power of AI models in functional genomics and sequence optimization

Results are specific to the institution where they were obtained and may not reflect the results achievable at other institutions.

For research use only, not for use in diagnostic procedures.

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Genes

Oligo Pools

NGS Target Enrichment Solutions

Variant Libraries

Synthetic Controls

Antibody Discovery

Building Better AI With High-Resolution Functional Genomics Data

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