From paper to excel sheets, electronic lab notebooks, and finally to the cloud, biomedical data has been going places in the last few decades! As we transition into a post-cloud world and all your data is on the cloud, any AI initiative will be at risk unless the data is clean and linked to each other. In this session, Abhishek Jha elaborates on how Elucidata is doubling and tripling down on a data-centric approach to accelerate biological discovery.
From paper to excel sheets, electronic lab notebooks, and finally to the cloud, biomedical data has been going places in the last few decades! As we transition into a post-cloud world and all your data is on the cloud, any AI initiative will be at risk unless the data is clean and linked to each other. In this session, Abhishek Jha elaborates on how Elucidata is doubling and tripling down on a data-centric approach to accelerate biological discovery.
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%.
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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.
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