报告人
李煜，香港中文大学计算机科学与工程系助理教授
报告题目
Complex disease modeling and efficient drug discovery with large language models
报告时间
2024年12月11日 9:00-10:00
报告地点
腾讯会议 840-911-624
报告摘要
Large language models, which can integrate and process large amounts of data in biomedicine, have great potential in modeling complex diseases and discovering functional biomolecules. Here, we showcase the potential with three examples. In the first example, we build a large language model trained on the insurance claims of around 123 million US people. With the model, we can give a unified representation of all the common complex diseases, which enables us to predict the genetic parameters of the diseases and discover unique genetic loci related to them efficiently. In the second example, we show how to utilize protein language models to discover remote homologs and functional peptides, such as signal peptides. With the model, we can discover diverse functional peptides with low sequence similarity against the known ones. In the final example, we show how to use the RNA language model to model the RNA sequence and structure relation, which enables us to perform RNA structure prediction and reverse design.
报告人简介
Yu Li is an Assistant Professor in the Department of Computer Science and Engineering at CUHK, leading the Artificial Intelligence in Healthcare (AIH) group. He is also the Visiting Assistant Professor at MIT/Harvard, working with Prof. James Collins. He works at the intersection between machine learning, healthcare and bioinformatics, developing new machine learning methods to resolve the computational problems in biology and healthcare, which leads to works published in top venues, such as Nature Biotechnology, Nature Methods, Nature Computational Science, and Nature Communications. In 2022, he was selected to the Forbes 30 Under 30 Asia list, Healthcare & Science. He obtained his Ph.D. in computer science from KAUST in 2020, after which he was nominated KAUST Alumni Change Makers Awards in 2022. Before that, he got the Bachelor degree in Biosciences with the First-class Honor from University of Science and Technology of China (USTC). He received Department Exemplary Teaching Award in 2024.
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