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."
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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.
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To advance the study, Wang and fellow researchers will develop two distinct computational tools:
- A scalable tool to analyze large genomic datasets and identify regulators key to ocular health and disease. "The algorithm needs to be scalable and run smoothly on very large collections of data," Wang said.
- A deconvoluting tool to address the complexity of eye tissue, which contains many different cell types with distinct functions. "When researchers analyze a whole tissue sample, the signals are mixed together," Wang said. "Our second tool will computationally separate those mixed signals to reveal how a regulator may behave in particular cell types without requiring very expensive single-cell experiments for every regulator."
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.
About The University of Texas at Arlington (UTA)
The University of Texas at Arlington is a growing public research university in the heart of Dallas-Fort Worth. With a student body of over 42,700, UTA is the second-largest institution in the University of Texas System, offering more than 190 undergraduate and graduate degree programs. Recognized as a Carnegie R-1 university, UTA stands among the nation's top 5% of institutions for research activity. UTA and its 300,000 alumni generate an annual economic impact of $28.8 billion for the state. The University has received the Innovation and Economic Prosperity designation from the Association of Public and Land Grant Universities and has earned recognition for its focus on student access and success, considered key drivers to economic growth and social progress for North Texas and beyond.
