Hye-Yeong Jo, phD

Hye-Yeong Jo, phD

Senior Manager, scientist

Research

Comprehensive multi-omics data collection from COVID-19 patients

Since the outbreak of COVID-19 pandemic in 2019, I’m leading a large-scale project, on a national scale, to comprehensively collect multi-omics dataset, including Whole-genome sequencing(WGS), HLA typing, bulk T/BCR-seq, single cell RNA sequencing (scRNA-seq) with T/BCR-seq at a single cell resolution, and expression profiling for 192 cytokines, as well as clinical data and laboratory testing data, through collaboration with 7 distinct hospitals in South Korea. Biospecimen, such as Peripheral blood mononuclear cells (PBMC), Plasma, Serum, Urine and NPS, is also being collected and deposited at National Biobank in Korea, and is distributed for approved researchers. Currently, additional proteomic and epigenetic data is being actively collected to make the immunological mechanisms by SARS-CoV-2 understand in detail, which , in turn, prevent upcoming virus in the future. It is expected that this project would provide invaluable insights and discoveries in the era of crisis caused by infectious diseases and understand detailed immunology in terms of host defensive mechanisms throughput multi-layer data.

Disease Risk Prediction using text-based heterogeneous dataset

Severity prediction against COVID-19 is crucial to perform early screening, detection and stratification of patients with COVID- 19. In this study, I, as a principle investigator (PI), developed machine learning (ML) models that predict COVID-19 severity based on clinical and laboratory testing data from 300 COVID-19 patients and 120 healthy controls. The proposed ML model with the selected features showed accurate prediction of COVID-19 severity. It is expected that this results provide evidence for not only efficient data- driven decision support to clinicians in urgent COVID-19 diagnostic situations, but also early detection and stratification of COVID-19 severity. Further, this study would be expanded into integrative approaches based on heterogeneous dataset, including high-dimensional omics to figure out remarkable markers for COVID-19 severity.