Beyond 3-D: USU Data Scientist Introduces Novel AI Tool to Interpret Complex Biological Data
Beyond 3-D: USU Data Scientist Introduces Novel AI Tool to Interpret Complex Biological Data
Publish Date: 2026-06-30 17:03:00
Source Domain: www.usu.edu
As humans, our eyes take in two-dimensional images our brains convert to three-dimensional experiences. This ability enables us to be aware of our position in space, judge distances, perceive depth and visually examine and enjoy all manner of objects and happenings.
But trying to envision sub-visible structures and high-dimensional processes that our human-engineered scopes can’t capture is a challenge for data scientists and visualization experts, who turn to machine learning and AI tools to amplify visual exploration.
“Biological processes are an example of complex, high-dimensional data,” says Kevin Moon, director of USU’s Data Science and Artificial Intelligence Center and associate professor in the Department of Mathematics and Statistics. “One of the datasets we’re using to test our AI tools, for example, is clinical data measured from multiple sclerosis patients. These datasets include hundreds of thousands of data points on disease progression at the cellular level, along with treatments and clinical outcomes.”
Moon is corresponding author on the paper, “Gaining Biological Insights through Supervised Data Visualization,” which was posted online June 30 in Nature Computational Science.
The paper was published in collaboration with:
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Lead author and USU alum Jake Rhodes (Ph.D.’22, statistics), assistant professor at Brigham Young University.
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Adele Cutler, professor emerita of USU’s Department of Mathematics and Statistics.
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Anhong Zhou, professor in USU’s Department of Biological and Chemical Engineering.
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USU alum Wei Zhang (Ph.D.’21, biological engineering), researcher at the University of Utah.
The team, whose research is supported by the National Institutes of Health and the IVADO Visiting Scholar Program, includes additional national and international collaborators.
“In this paper, we introduce RF-PHATE, which is an acronym for Random Forest-Potential of Heat-diffusion for Affinity-based Trajectory Imbedding,” Moon said. “That’s a…
