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AI will not replace scientists. It will expand the reach, pace, and ambition of science itself. I believe we are on the cusp of a transformation in the way science is done. Just as personal computers changed the way we write and the internet changed the way we collaborate, AI is changing the way we work with data. In biology and medicine, this change is long overdue.

Most platforms weren’t built for bioinformatics. Manifold supports unified data, reusable pipelines, and built-in governance—so scientific teams can work faster, collaborate securely, and trust their infrastructure.

As the research community continues to rely on data shared by the National Institutes of Health (NIH), ensuring the security of that data continues to be an important requirement. Last year, the NIH issued an implementation update to the NIH Genomic Data Sharing Policy (GDS Policy), which outlines requirements for researchers accessing, storing, or processing large-scale human and non-human genomic data generated from NIH-funded research. The update was designed to continue to promote responsible data management and access under the GDS Policy and ensure the broad and responsible sharing of NIH genomic data for research that advances human health. 

When we began exploring the collaboration between Manifold and the Broad Institute, we knew this had to be more than a transactional relationship. From the outset, trust and a shared mission to accelerate the pace of biomedical science guided our efforts.

Join experts from Indiana University, Manifold, and Snowflake for an on-demand eWebinar where you'll learn how to break down data silos, integrate and analyze large-scale clinical and multi-modal data, and collaborate securely with both internal teams and external institutions. Discover how to leverage AI to automate data operations and surface insights faster, enabling more efficient and impactful research.

The landscape of cancer research is rapidly evolving with the integration of multimodal data, including tissue analysis, clinical records, genomic sequencing, and imaging. By harnessing this rich array of cancer-specific data, researchers gain a comprehensive understanding of disease, leading to more accurate diagnoses and personalized treatments.

Observational studies are pivotal in driving cancer breakthroughs, providing insights into the environmental, lifestyle, and genetic factors that influence cancer development and prevention. This real-world data is the cornerstone of targeted prevention strategies, leading to life-saving discoveries.

Time-to-insight… It’s a metric you don’t hear about much. But it’s a lurking factor throttling the rate of scientific progress in cancer research. So what’s holding us back?

AI will not replace scientists. It will expand the reach, pace, and ambition of science itself. I believe we are on the cusp of a transformation in the way science is done. Just as personal computers changed the way we write and the internet changed the way we collaborate, AI is changing the way we work with data. In biology and medicine, this change is long overdue.

Most platforms weren’t built for bioinformatics. Manifold supports unified data, reusable pipelines, and built-in governance—so scientific teams can work faster, collaborate securely, and trust their infrastructure.

As the research community continues to rely on data shared by the National Institutes of Health (NIH), ensuring the security of that data continues to be an important requirement. Last year, the NIH issued an implementation update to the NIH Genomic Data Sharing Policy (GDS Policy), which outlines requirements for researchers accessing, storing, or processing large-scale human and non-human genomic data generated from NIH-funded research. The update was designed to continue to promote responsible data management and access under the GDS Policy and ensure the broad and responsible sharing of NIH genomic data for research that advances human health. 

When we began exploring the collaboration between Manifold and the Broad Institute, we knew this had to be more than a transactional relationship. From the outset, trust and a shared mission to accelerate the pace of biomedical science guided our efforts.

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The landscape of cancer research is rapidly evolving with the integration of multimodal data, including tissue analysis, clinical records, genomic sequencing, and imaging. By harnessing this rich array of cancer-specific data, researchers gain a comprehensive understanding of disease, leading to more accurate diagnoses and personalized treatments.

Observational studies are pivotal in driving cancer breakthroughs, providing insights into the environmental, lifestyle, and genetic factors that influence cancer development and prevention. This real-world data is the cornerstone of targeted prevention strategies, leading to life-saving discoveries.

Time-to-insight… It’s a metric you don’t hear about much. But it’s a lurking factor throttling the rate of scientific progress in cancer research. So what’s holding us back?