Time: 9:30-10:30am, Dec. 10th, 2019, Tuesday
Venue: RoomA1514, Science Building, North Zhongshan Road Campus
Spreker: JingSi Ming, post-doctoral fellow, The Hong Kong University of Science and Technology
Abstract： To analyze the large scale genomic data, efficient statistical and computational methods need to be proposed in order to provide deeper insights on biological problems, including genetic architecture of complex traits, cell biology and gene regulation. In this talk, I’ll introduce two integration methods. One is to characterize the relationship among complex traits by integrating summary statistics from multiple genome-wide association studies (GWASs) and functional annotations. The other is to integrate heterogeneous single-cell RNA-sequencing (scRNA-seq) data sets across multiple platforms.
Speaker’s Bio：Dr. Jingsi Ming is a post-doctoral fellow in Department of Mathematics at The Hong Kong University of Science and Technology University. She obtained her Ph.D. degree from Hong Kong Baptist University in 2018. Her research interest is in data science with statistical machine learning and deep learning.
