2020年11月30日(星期一)上午9:00生科楼2018 “111引智计划”专家报告(可通过zoom线上参加,会议ID:83634509989)
发布日期:2020-11-17 浏览次数:  信息来源:生物学院



“111引智计划”专家报告


题目Leveraging High-dimensional Longitudinal Big Data to Study RNA Regulation

主讲人:Dr. Peng Jiang (江澎)

时间:2020年11月30日(星期9:00

地点:生科楼2018会议室                      

(可通过zoom线上参加,会议ID83634509989

邀请人:于舒洋      62731142-2003

个人简介

教育经历

2012-Present Computational Biologist, Morgridge Institute for Research, Madison, WI, USA

2008-2012 Postdoctoral Fellow, Department of Internal Medicine, University of Iowa, IA, USA

2003-2007 Ph.D. Department of Biomedical Engineering, Southeast University, China

1999-2003 B.S.Department of Biomedical Engineering, Southeast University, China

 

研究方向

Single-cell RNA-seq, ATAC-seq, Machine learning, Network analysis, Integrative “omics” data analysis, Time series data analysis, Volumetric muscle loss (VML), Mouse digit tip regeneration, Type 2 diabetes.


主持项目

美国国防部高级研究计划局 (DARPA) - BETR program (Subaward PI)

美国国立卫生研究院 (NIH) - 5U24HL134763 (Subaward Co-I)

ABSTRACT (报告简要)

The advent of high-throughput sequencing (HTS) based techniques (e.g., RNA-seq, SELEX-seq, and CLIP-Seq) have fundamentally changed the way we examine the molecular basis underlying human health and diseases. However, how to maximize and accelerate the utility of this big data in biomedical research is a big problem. In this talk, I will discuss my efforts towards developing statistical and computational methods for leveraging massive multi-source high-dimensional longitudinal omics data to systematically investigate the variation and dynamics of gene regulation in development, neural toxicity prediction, and tissue regeneration. I will conclude by discussing my ongoing work that integrate machine learning, network analysis and recommender system for data prioritization and prediction, which should further allow us to maximize and accelerate the utility of multi-dimensional Omics data for biomedical research.




欢迎各位老师同学参加!


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