My research includes developing the Breast Cancer Proteome Portal that collectively visualizes protein and transcript abundance relationships in the three major clinical breast cancer proteomics studies. This tool is applied to explore the top-correlating proteins/transcripts with proteins/transcripts of interests and garner insights into the compartmentalization of one-carbon metabolism and the regulation of glutamine anaplerosis. Currently, I am producing a machine-learning model that can predict protein localization using the correlation between protein and transcript abundance and protein metadata.
2021, MS, Biochemical and Molecular Nutrition, Tufts University Friedman School of Nutrition Science and Policy
2018, BS, Food Science and Technology, University of California, Davis