Implementing FAIR principles is critical for reusing legacy and newly generated data for tackling high-value healthcare challenges. The prominent panelists in our most recent panel discussion, "Making the Case for F.A.I.R in Biopharma R&D," address FAIR concepts that can advance your data strategy.
Key Questions Answered:
Implementing FAIR principles is critical for reusing legacy and newly generated data for tackling high-value healthcare challenges. The prominent panelists in our most recent panel discussion, "Making the Case for F.A.I.R in Biopharma R&D," address FAIR concepts that can advance your data strategy.
Key Questions Answered:
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.