报告题目：Unagi reconstructs the cellular dynamics in pulmonary fibrosis and identifies repurposed drugs
报告人：丁俊博士，加拿大麦吉尔大学医学院助理教授，FRQS人工智能医疗中心青年学者
报告时间：2023年7月7日（周五）15:00
报告地点：金沙js55登陆入口主站办公楼310会议室
报告摘要：Idiopathic Pulmonary fibrosis (IPF) is a terminal chronic lung disease causing lung scarring and a progressive decline in lung function. Current medications for this disease are minimal (Pirfenidone and Nintedanib). Emerging single-cell sequencing technologies can track the cellular dynamics in IPF progression and thus provide unrivaled opportunities to identify more effective therapeutic targets and drugs. In this paper, we have profiled the cellular states across different IPF stages using single-nuclei RNA-seq. Furthermore, we have developed a unified and computationally efficient drug repurposing framework called UNAGI (computational approach driven repurposed drugs for idiopathic pulmonary fibrosis), which reconstructs the cellular dynamics from the IPF single-nuclei RNA-seq data and identifies candidate drugs for the disease. UNAGI employs a deep generative adversarial variational-autoencoder with graph embedding to iteratively learn cellular dynamic graphs of IPF progressions and suggest a list of potential therapeutic targets from the reconstructed gene regulatory network that modulate the disease progression. UNAGI empowers in-silico explorations of intervention strategies to restore the healthy status of dynamic cell populations during the disease progression, which presents a short list of target pathways, potential repurposed drugs, and novel compounds against IPF. The UNAGI platform successfully identifies Nintadanib as an efficacious IPF drug and identifies several other potential compounds previously reported to repress induced pulmonary fibrosis. We have also systematically examined the top pathways identified by the model, which are significantly associated with pulmonary fibrosis as documented in the existing literature. These all manifest the effectiveness of the UNAGI platform.
报告人简介：Jun Ding (https://www.meakinsmcgill.com/ding/) is an Assistant Professor in the Department of Medicine at McGill University Health Centre since March 2021. He is also a FRQS Junior 1 scholar in AI healthcare. Previously, Jun completed his postdoctoral training at the Computational Biology Department, School of Computer Science, Carnegie Mellon University, under the guidance of Dr. Ziv Bar-Joseph. In 2016, he obtained his Ph.D. in Computer Science from the University of Central Florida.
Jun's research primarily revolves around the development of computational methods to gain insights into cellular dynamics across various biological processes using single-cell multi-omics data. Leveraging the power of single-cell and machine learning technologies, his research focuses on understanding disease progression and identifying drugs and compounds for pulmonary diseases. Jun has published over 30 papers in leading computational biology journals, including Genome Research and Cell Stem Cell, with a significant portion dedicated to machine learning approaches for decoding cellular dynamics from single-cell datasets. Many of his computational models have paved the way for significant advancements in the field of biomedicine.
