Duke University School of Medicine has announced a partnership with Network Bio to generate molecular datasets that researchers can use to develop artificial intelligence models for disease detection, treatment selection and drug discovery.
The collaboration will draw on Duke’s biobanks, using de-identified samples and data from research participants who have provided consent. Molecular information generated from the stored samples will be paired with de-identified clinical records.
The resulting multi-omics datasets are intended to support models that identify disease earlier, help match patients with appropriate therapies and reveal potential drug targets.
The partnership also aims to expand researchers’ access to advanced molecular profiling and sequencing, which can be costly for academic research teams. By supporting dataset generation and associated research infrastructure, the collaboration is designed to broaden opportunities for translational work.
Svati Shah, the Ursula Geller Distinguished Professor of Cardiology and director of Duke’s Center for Precision Health, said the work could help researchers identify biomarkers, patient subgroups and therapeutic targets more quickly.
Those findings would provide starting points for further clinical validation. The partnership’s stated objectives focus on creating research resources and models that can inform the development of more precise approaches to treatment.
Duke identified its clinical expertise, biobank collections, integrated research infrastructure and capabilities in molecular research and data science as foundations for the collaboration.
The announcement also points to expanded funding and infrastructure opportunities for investigators. Linking molecular measurements with clinical histories will allow research teams to study the relationship between biological characteristics, disease patterns and treatment responses within the resources covered by the collaboration.
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“The partnership will enable advanced molecular profiling and molecular sequencing, which can be costly for academic researchers.”
Svati Shah, Ursula Geller Distinguished Professor Of Cardiology And Director Of The Center For Precision Health At Duke
“The work could identify biomarkers, patient subgroups, and therapeutic targets faster, creating a stronger path to clinical validation.”
Svati Shah, Ursula Geller Distinguished Professor Of Cardiology And Director Of The Center For Precision Health At Duke
