The Cube Initiative is a key strategic innovation for JAX researchers, and ultimately for scientists worldwide, in which we are building a next generation, digital platform deeply integrating multimodal human and mouse data to understand genetic complexity, create predictive models, and uncover new points of therapeutic intervention for diseases.

Twenty years ago, scientists sequenced the first human genome, with the promise of precision medicine: armed with this new information, we would be able to identify precise, therapeutic targets for all kinds of diseases.

Since then, tremendous advances in sequencing and computing technology have resulted in the cataloging of more than 250,000 unique risk variants associated with disease.

However, this effort has not resulted in cures for common diseases like cancer, Alzheimer’s, or diabetes. Why? Because of the incredible genetic complexity underlying all human diseases.

What’s next? We have to unpack this complex web if we want to make progress with precision medicine. At JAX, we focused on making precision medicine a reality through our predictive biology initiatives. The first step in providing a roadmap towards this goal is The Cube Initiative. The Cube Initiative brings together JAX’s power to scale, iterate and interrogate data across humans and mouse models. Here’s how it works:

Download the JAX white paper on predictive biology.

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Predictive biology initiatives: Collaborate with us

Big ideas require partnership. To leverage the team-science approach at The Jackson Laboratory, the Laboratory welcomes opportunities to collaborate on The Cube Initiative and other projects with researchers and scientists in support of the research and data tools required to drive predictive biology. 

To learn more or inquire about predictive biology collaboration opportunities, please email

Predictive Biology

Predictive Biology

Proof of concept

Learn about our two-year project focused on type 2 diabetes.

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Meet the team

Meet the scientific minds who are making predictive biology possible.

Meet the team
Predictive Biology

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