|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）|
标题问题: Leveraging High-dimensional Longitudinal Big Data to Study RNA Regulation
主讲人: Dr. Peng Jiang （江澎）
约请人: 于舒洋 62731142-2003
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
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.
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)
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.