题目：Leveraging High-dimensional Longitudinal Big Data to Study RNA Regulation
主讲人：Dr. Peng Jiang （江澎）
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)
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.