Support for Precision Medicine Study Using Artificial Intelligence and Statistics
Researchers from the University of Texas and UT Southwestern have received a multimillion-dollar grant to predict gene and drug synergies in precision medicine applications.
Researchers from the University of Texas at Arlington (UTA) and UT Southwestern have received a $3.1 million grant to predict gene and drug synergies using artificial intelligence and Bayesian statistics, aiming to accelerate the development of targeted therapies.
A New Computational Framework for Precision Medicine
Junzhou Huang, a professor of computer science and engineering at the University of Texas at Arlington, is leveraging artificial intelligence to better predict how genes and drugs can work together in treating diseases.
Project Funded by the National Institutes of Health
The $3.1 million grant provided by the National Institutes of Health will support the project jointly led by Professor Huang, UTA Mathematics Professor Xinlei Wang, and UT Southwestern Assistant Professor Lin Xu.
Interinstitutional Collaboration
This partnership brings together the UTA College of Engineering, the UTA College of Science, and UT Southwestern Medical Center, combining expertise in artificial intelligence, Bayesian statistics, and computational biology.
Advanced Methods and Validation
The researchers will apply multimodal large language models and Bayesian statistical modeling to improve gene function prediction, and validate the predictions through high-throughput screening experiments.