Soun-Mee Lee
Soun-Mee Lee
Sang Mee Lee, PhD
Research Associate (Assistant Professor)
Department of Nursing, Semyoung University, Jecheon, South Korea
South Maryland Ave.
MC2000, R - 314
Chicago, IL 60637
(773) 834-6765
Biography
Dr.Soun-Mee Lee is a Research Associate (Assistant Professor) at the Department of Nursing, Semyoung University, Jecheon, South Korea. Soun-Mee Lee is the Editorial Board Member of many peer reviewed journals and her area of expertise, as a Research Scholar credits her with many publications in national and international journals. She is committed to highest standards of excellence and it proves through his co-authorship of many books.
Research Interest
Major satisfaction; Group identification; Nursing students
Sang Mee Lee’s research interests are comprised of statistical genetics and applied statistics to biological and medical areas. These interests are paired with health in order to answer the health related questions with statistical evidence. Sang Mee Lee is part of the University of Chicago’s Biostatics Team and her research projects mainly include the following:
Gene expression data analysis: Sang Mee’s doctoral dissertation research was mainly in Gene Set Enrichment Analysis (GSEA). She suggested a novel likelihood based approach using a finite mixture model and illustrated the competitive performance of the proposed method through extensive simulation studies and application to gene expression data. She continues to develop and expand this method to evaluate more general clinical outcomes of interest.
Diabetes Translation Research: As the principal biostatistician of the Chicago Center for Diabetes Translation Research, Sang Mee works to improve diabetes care and outcomes for vulnerable populations with diabetes. One of this group’s main goals is to help reduce racial and ethnic disparities in care.
Cancer Studies: One of the main projects that Sang Mee is a part of, is about hematologic malignancies with identification of high-risk human leucocyte antigen (HLA) in unrelated donor hematopoietic cell transplantation. The group evaluated about 400 amino acid substitution positions and types using a combination of Random Forest and logistic regression. This demonstrated a potential shift in the approach currently used for selection of mismatched donors to a more defined structural approach.
