UA Little Rock receives nearly $450,000 to develop deep learning methods to identify cells that advance complex diseases

Dr. Mary Yang.

A University of Arkansas at Little Rock professor has received a $443,854 grant from the National Institutes of Health to develop unique deep learning methods to identify key cell networks in complex diseases. 

Dr. Mary Yang, professor of information science and director of the Midsouth Bioinformatics Center at UA Little Rock, will conduct research that will help doctors and scientists further understand how complex diseases evolve and develop in the body as well as how to identify effective drug targets.

The three-year grant, “Develop Novel Deep Learning and Combinatorial Optimization Methods to Identify Key Disease Regulatory Elements for Single-Cell Data,” will be complete on July 30, 2023. In addition to its potential contributions to health research, the project will give students valuable experience in developing computational approaches to biomedical problems.

“The project will serve as a vehicle to equip undergraduate and graduate students with essential research skills and interdisciplinary knowledge and to stimulate the students’ ambition to pursue careers in biomedical science,” Yang said. 

Yang’s deep learning model focuses on developing high-resolution, single-cell genomic analytics techniques to capture cell differences with detail and clarity. By clearly characterizing cell differences, scientists can better identify cells that cause diseases to advance and evolve. This technique will allow more specialized, targeted treatments to different cells in the body. 

Yang will supervise undergraduate students from different disciplines as well as graduate students in the joint UA Little Rock-UAMS Bioinformatics program during this project. She is collaborating with Dr. Sherman Weissman, a professor of genetics at the Yale University School of Medicine, who will provide experimental validation of the research models created at UA Little Rock.

The MidSouth Bioinformatics Center at UA Little Rock provides extensive bioscience computational resources and training to faculty, staff, and students in the region. 

Research reported was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R15GM137288. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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