Degui Zhi
Associate Professor
The University of Texas Health Science Center at Houston
School of Biomedical Informatics
I am interested in computational aspects of precision medicine. Especially, I develop and apply methods for the analysis of modern biomedical big data that integrate statistical and informatics skills. Current interests in my lab include:
Development of methods for genome informatics for large population genotypes
We develop efficient genome informatics methods for extracting information from population whole genome sequencing (WGS) data for population genetics and disease association studies. For example, we improve variant calling and haplotype phasing from WGS data, represent phasing uncertainty in analyses, and extract chromosome segment sharing among individuals in an efficient manner.
Application of bioinformatics and statistical genetic analysis for large cohort association studies
My applied research focuses on leading the design and execution of the Omics analysis of several large epidemiological cohorts, including GWAS, EWAS, exome, and whole genome sequencing. I am a senior member of several cardiovascular epidemiological studies. I serve as a member of the NHLBI Trans-Omics for Precision Medicine (TOPMed) analysis committee.
Machine learning approaches for modeling Omics and clinical informatics data
An emerging interest in my lab is to use deep learning approaches for predicting phenotypes from Omics data and electronic health records.
A tutorial in my lab would involve learning 1) basic analytic literacy skills such as R and Python. 2) tools and pipelines for analyzing big data from real large epidemiological cohorts; 3) development and evaluating methods for genome informatics.
Education & Training
Ph.D. - University of California, San Diego - 2006