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Description

Artificial intelligence (AI) is transforming biomedical research by enabling the analysis of complex biological data and accelerating discoveries across multiple scales of biological organization. Recent advances in deep learning and foundation AI models have created new opportunities to predict molecular structures and interactions, interpret cellular imaging data, reconstruct gene regulatory networks, and integrate heterogeneous omics datasets. In this talk, I will present several AI methods and tools developed in our lab to address these challenges. First, I will discuss AI approaches for predicting protein structures, functions, and interactions, with potential applications to understanding disease mechanisms, identifying therapeutic targets, and designing drugs. Second, I will introduce deep learning methods for analyzing cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) data to facilitate molecular structure determination and cellular structural analysis. Third, I will present AI approaches for inferring gene regulatory networks to better understand transcriptional regulation and its roles in biological processes and diseases. Finally, I will discuss AI methods for integrating multi-omics data to uncover complex biological relationships. Through these examples, I will highlight the capabilities of AI for analyzing diverse biological data and discuss opportunities for applying these technologies to biomedical research and precision health.

Speaker 

Jianlin (Jack) Cheng is a Curators’ Distinguished Professor and Paul K. and Diane Shumaker Professor in Bioinformatics in the Department of Electrical Engineering and Computer Science at the University of Missouri. He received his PhD in information and computer science from the University of California, Irvine in 2006. His research is focused on developing machine learning and artificial intelligence (AI) methods and tools for big biomedical data analysis. His research group has developed numerous bioinformatics tools for analyzing protein structure and function, biological networks, and 3D genome structure, which are used by scientists around the world. His research has been supported by the National Institutes of Health (NIH), the National Science Foundation (NSF), and the U.S. Department of Energy (DOE). Cheng was a recipient of the NSF CAREER award and the MU College of Engineering’s junior and senior faculty research awards.