Researchers at The University of Texas at Arlington are developing AI-powered computational tools that can identify and characterize the biological “control switches” that help keep eyes healthy and reveal what causes eye disease.
The four-year project is funded by a $1.96 million grant from the National Eye Institute and aims to contribute to the development of targeted therapies to improve and preserve vision.
“The eye contains many specialized tissues and cell types that work together to make vision possible,” said Xinlei (Sherry) Wang, the Jenkins Garrett Professor of statistics and data science in UT Arlington’s Department of Mathematics. “Understanding the complex relationship between tissue-specific transcriptional regulators and visual function is a cornerstone of vision research.”
Dr. Wang collaborates with Lin Xu, an assistant professor at UT Southwestern. Their teams combine expertise in Bayesian statistics, bioinformatics, single-cell sequencing, omics data analysis, ocular biology and clinical experience.
Morteza Khaledi, dean of the College of Science, called the grant “an outstanding achievement and a testament to the strength, significance and growing impact of Dr. Wang’s interdisciplinary research program.”
The award builds on Wang’s work leveraging AI and advanced statistical methods to help scientists better understand complex diseases. Her research focuses on creating computational tools that uncover important biological patterns hidden within massive datasets, helping researchers identify promising targets for future study and treatment.
To advance the study, Wang and fellow researchers will develop two distinct computational tools:
By pinpointing key regulators, Wang said, the tools will help researchers focus on the most promising targets, saving time and resources.
“Together, these two tools will let us study the eye at two levels: the overall tissue level and the individual cell-type level within it,” Wang said. “The long-term goal is to improve understanding of eye disease mechanisms and help the field identify promising directions for future diagnostics and targeted therapies.”
Wang is co-leading a related project with UTA College of Engineering Professor Junzhou Huang on a study that combines AI and Bayesian learning to speed drug design.