May 29, 2023
School of Biomedical Sciences is pleased to invite you to join the following seminar:
Date: 29 May 2023 (Monday)
Time: 4:00 pm – 5:00 pm
Venue: Lecture Theatre 1, G/F, William M.W. Mong Block, 21 Sassoon Road
Speaker: Professor Xuegong Zhang, Professor of Pattern Recognition and Bioinformatics, Department of Automation, Tsinghua University
Talk Title: Learning representations of cell atlas data
Xuegong Zhang received his BS degree in Industry Automation in 1989 and his Ph.D. degree in Pattern recognition and Machine Intelligence in 1994, both from Tsinghua University, after which he joined the faculty of Tsinghua University. He had visited Harvard School of Public Health in 2001-2002, and is now a Professor of Pattern Recognition and Bioinformatics in the Department of Automation, Tsinghua University, and Adjunct Professor of the School of Life Sciences and School of Medicine. He is ISCB Fellow and CAAI Fellow. He is also the chairman of the Committee of Bioinformatics and Artificial Life, Chinese Association of Artificial Intelligence, and the chairman of the Committee of Intelligent Health and Bioinformatics, Chinese Association of Automation. His major research interests include machine learning, bioinformatics, human cell atlas, and intelligent precision medicine.
Building an atlas of all human cell types with their gene expression properties at single-cell resolution can provide a fundamental reference to future human biology and medicine. Cells exhibit multifaceted heterogeneities at multiple scales. This talk will introduce our efforts toward learning representations of cell atlas data to capture these heterogeneities. We developed a multidimensional coordinate system UniCoord for different known attributes of cells by adopting a supervised variational autoencoder (VAE) neural network model. It can capture key cellular features of spatial, temporal and functional gradients from massive data. It provides a prototype for a learnable universal coordinate framework for organizing sophisticated cell atlases. We also developed a method STEM for mapping scRNA-seq data and spatial transcriptomics (ST) data by learning a shared embedding spaces of the two types of data.
ALL ARE WELCOME
Should you have any enquiries, please feel free to contact Miss Angela Wong at 3917 9216.