Clinical and real-world datasets hold enormous potential for translational research, but their complexity makes them hard to use. A single patient record can span labs, imaging, omics, and outcomes-yet much of this information is fragmented, inconsistently defined, and difficult to unify. Most EHRs capture little more than demographics and diagnoses, leaving critical measurement data out of reach.
Polly KG solves this by seamlessly integrating molecular data with patient data records, transforming scattered datasets into a living, semantically rich knowledge graph. It combines curated proprietary knowledge with high-quality public sources, ensuring both coverage and scientific rigor. The system is immediately usable yet fully customizable, so teams can adapt scoring logic, ontologies, and cohort definitions to their needs.
Unlike rigid platforms, Polly KG keeps raw data structured while surfacing analysis-ready features-like biomarker associations, outcomes, and differential expression-that can be queried in plain English. With built-in provenance and confidence scores, it helps organizations accelerate discovery, design smarter cohorts, and reach trial readiness in months, not years.
Clinical and real-world datasets hold enormous potential for translational research, but their complexity makes them hard to use. A single patient record can span labs, imaging, omics, and outcomes-yet much of this information is fragmented, inconsistently defined, and difficult to unify. Most EHRs capture little more than demographics and diagnoses, leaving critical measurement data out of reach.
Polly KG solves this by seamlessly integrating molecular data with patient data records, transforming scattered datasets into a living, semantically rich knowledge graph. It combines curated proprietary knowledge with high-quality public sources, ensuring both coverage and scientific rigor. The system is immediately usable yet fully customizable, so teams can adapt scoring logic, ontologies, and cohort definitions to their needs.
Unlike rigid platforms, Polly KG keeps raw data structured while surfacing analysis-ready features-like biomarker associations, outcomes, and differential expression-that can be queried in plain English. With built-in provenance and confidence scores, it helps organizations accelerate discovery, design smarter cohorts, and reach trial readiness in months, not years.
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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