Dr. Zhao received her Ph.D degree from the Department of Materials Science and Engineering, University of Central Florida. From University of Iowa, she received her M.S. degree in Statistics from the Department of Statistics and Actuarial Science. She received her B.S. degree in Polymer Science and Engineering from Tsinghua University, China. She was a researcher at the R&D department, Applied Materials. Inc, and a clinical assistant professor at the Department of Biomedical Informatics (BMI) at the Ohio State University. Zhao’s research is focused on developing and applying statistical methods of high-dimensional data integration to address impactful biomedical problems.
Dr. Zhao has tremendous experience working collaboratively with basic science researchers and physicians for designing optimal multi-omics data collection and performing sophisticated analyses, including but not limited to genomics, transcriptomics, proteomics, metabolomics, CRISPR-based genetic screening, and third generation sequencing.
To make meaningful contributions towards solving biomedical problems, Zhao is dedicated to delivering highly reliable predictions (e.g., predictive biomarkers and therapeutic target genes) by integrating all available data. In her most recent project, she developed a statistical method called CEDA (CRISPR screen with Expression Data Analysis) to integrate gene expression profiles with CRISPR pooled screen data to identify essential genes with higher abundance. CEDA pools the single guide RNAs (sgRNAs) targeting the genes with similar abundance to fit a hierarchical model, which can improve parameter estimation and reduce false positive rate significantly. CEDA is the first method that statistically integrates gene abundance into the target prediction model of genetic screen data. It lays a solid foundation for future development of integrative analysis methods for CRISPR screens.
In parallel, Zhao adopted a joint-pathway analytic strategy to integrate plasma proteomics profiles and metabolomics data at the individual patient’s pathway level aiming to identify predictive biomarkers of glucocorticoids sensitivity in nephrotic pediatric patients. This data analysis strategy allows in-depth understanding of disease mechanisms.