As single-cell technologies evolved over years, diverse single-cell atlas datasets have been rapidly accumulated. Integrative analyses harmonizing such datasets provide opportunities for gaining deep biological insights. In this talk, we present a computational approach developed for fast and accurate integration of large-scale single-cell atlases. Our method incorporates generative adversarial networks and auto-encoder structures into a unified framework. Through integration of numerous datasets, we show that our method outperforms other state-of-the-art methods in terms of scalability and accuracy.
5月4日
4:00pm - 5:00pm

地点
https://hkust.zoom.us/j/92441893149 (Passcode: 538242)
讲者/表演者
Miss Jia ZHAO
主办单位
Department of Mathematics
联系方法
付款详情
对象
Alumni, Faculty and staff, PG students, UG students
语言
英语
其他活动

3月24日
研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - Pushing the Limit of Nonlinear Vibrational Spectroscopy for Molecular Surfaces/Interfaces Studies
Abstract
Surfaces and interfaces are ubiquitous in Nature. Sum-frequency generation vibrational spectroscopy (SFG-VS) is a powerful surface/interface selective and sub-monolayer sensitive spect...

11月22日
研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - Leveraging Protein Dynamics Memory with Machine Learning to Advance Drug Design: From Antibiotics to Targeted Protein Degradation
Abstract
Protein dynamics are fundamental to protein function and encode complex biomolecular mechanisms. Although Markov state models have made it possible to capture long-timescale protein co...