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.<\/p>\n
But trying to envision sub-visible structures and high-dimensional processes that our human-engineered scopes can\u2019t capture is a challenge for data scientists and visualization experts, who turn to machine learning and AI tools to amplify visual exploration.<\/p>\n
\u201cBiological processes are an example of complex, high-dimensional data,\u201d says Kevin Moon, director of USU\u2019s Data Science and Artificial Intelligence Center<\/a> and associate professor in the Department of Mathematics and Statistics<\/a>. \u201cOne of the datasets we\u2019re 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.\u201d<\/p>\n Moon is corresponding author on the paper, \u201cGaining Biological Insights through Supervised Data Visualization,\u201d which was posted online June 30<\/a> in Nature Computational Science.<\/p>\n The paper was published in collaboration with:<\/p>\n Lead author and USU alum Jake Rhodes (Ph.D.\u201922, statistics), assistant professor at Brigham Young University.<\/p>\n Adele Cutler, professor emerita of USU\u2019s Department of Mathematics and Statistics.<\/p>\n Anhong Zhou, professor in USU\u2019s Department of Biological and Chemical Engineering<\/a>.<\/p>\n USU alum Wei Zhang (Ph.D.\u201921, biological engineering), researcher at the University of Utah.<\/p>\n