Professor Wei Pan

PUBHL Biostatistics Division
School of Public Health
Twin Cities
Project Title: 
Computational Genomics/Genetics and Neuroimaging

These researchers develop and evaluate various statistical and computational methods for genome-wide association studies (GWASs), next-generation sequencing data, multiple types of -omic data, and neuroimaging data. They conduct frequent and wide-ranging studies with large-scale simulated and real genetic and -omic data, which require both CPU-intensive computing and a large disk space to store big data such as the U.K. Biobank data of GWAS, WES, imaging, and phenomes. More recently the researchers have expanded their study into neuroimaging involving large-scale MRI data and deep learning. For example, they would like to develop and evaluate new computational methods to construct functional networks or connectomes based on resting-state functional MRI (fMRI) data. One of their main goals is to integrate multiple types and multiple sources of -omic and neuroimaging data, for which they will continue downloading huge datasets such as the U.K. Biobank GWAS and neuroimaging data with almost half a million participants.

Research by this group was featured on the MSI website in April 2023: Improving Imaging Wide Association Studies.

Project Investigators

Yuanhao Cai
Dr. Dipnil Chakraborty
Lei Fang
Jinwen Fu
Ruoyu He
Katherine Knutson
Hyun Jung Koo
Alexander Kuhn
Jiakun Li
Huiling Liao
Zhaotong Lin
Mykhaylo Malakhov
Faye Norby
Nidhi Pai
Professor Wei Pan
Pratik Ramprasad
Jingchen Ren
Charles Spanbauer
Peiyao Wang
Xieheng Wang
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