2020年11月30日(礼拜一)上午9:00生科楼2018 “111引智打算”专家报告(可经由过程zoom线上参与,集会ID: 83634509989)
发布日期: 2020-11-17 阅读次数:   信息来源:瑞典1分彩



“111引智打算”专家报告


标题问题: Leveraging High-dimensional Longitudinal Big Data to Study RNA Regulation

主讲人: Dr. Peng Jiang (江澎)

时候: 2020年11月30日(礼拜9:00

地址: 生科楼2018集会室                      

(可经由过程zoom线上参与,集会ID: 83634509989

约请人: 于舒洋      62731142-2003

小我简介

教导履历

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

 

研讨标的目的

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.


掌管名目

美国国防部高等研讨打算局 (DARPA) - BETR program (Subaward PI)

美国国立卫生研讨院 (NIH) - 5U24HL134763 (Subaward Co-I)

ABSTRACT (报告扼要)

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.




接待列位教员同窗参与!


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