Sangmi Lee Headshot

Sangmi Lee

Research Fellow in Biomedical Informatics

Sangmi Lee earned her PhD from the Korea Advanced Institute of Science and Technology (KAIST) in South Korea, where her research focused on the development and application of systems biology approaches for the discovery of cancer biomarkers and drug targets tailored to personalized cancer treatment. She developed computational methods to elucidate altered cancer metabolism and identify metabolism-based drug targets for high-risk cancer patients, employing network modeling, statistics, and artificial intelligence. These endeavors in deciphering the complex metabolic underpinnings of cancer lay the groundwork for developing therapies that are more effective and less toxic, thereby profoundly impacting patient care. Her work in computational biology, marked by a commitment to integrating multi-omics data and mathematical methodologies, underscores her continuous dedication to addressing medical challenges in cancer research. Sangmi is now focused on exploring the genomic underpinnings of cancer and its metabolism, drawing on her extensive expertise in bioinformatics, computational modeling, optimization techniques, and machine learning. 

Analgesic effects of ultrasound-guided preoperative posterior Quadratus Lumborum block in laparoscopic hepatectomy: A prospective double-blinded randomized controlled trial.
Authors: Lee S, Ko JS, Kang R, Choi GS, Kim JM, Gwak MS, Shin YH, Lee SM, Kim GS.
J Clin Anesth
View full abstract on Pubmed
Effect of high-dose intravenous iron injection on hepatic function in a rat model of cirrhosis.
Authors: Kwon JH, Kang R, Lee SM, Hahm TS, Cho HS, Jin G, Ko JS.
J Int Med Res
View full abstract on Pubmed
Association between Preoperative C-Reactive Protein-to-Albumin Ratio and Mortality after Plastic and Reconstructive Surgery.
Authors: Oh AR, Sung HM, Park J, Jin G, Kong SM, Jung M, Lee SM.
J Clin Med
View full abstract on Pubmed
Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data.
Authors: Lee G, Lee SM, Lee S, Jeong CW, Song H, Lee SY, Yun H, Koh Y, Kim HU.
Genome Biol
View full abstract on Pubmed
Does coronavirus disease 2019 history alone increase the risk of postoperative pulmonary complications after surgery? Prospective observational study using serology assessment.
Authors: Oh AR, Kang ES, Park J, Lee SM, Jeong M, Lee JH.
PLoS One
View full abstract on Pubmed
Preoperative C-reactive protein/albumin ratio and mortality of off-pump coronary artery bypass graft.
Authors: Oh AR, Kwon JH, Park J, Min JJ, Lee JH, Yoo SY, Lee DJ, Kim W, Cho HS, Kim CS, Lee SM.
Front Cardiovasc Med
View full abstract on Pubmed
Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes.
Authors: Song S, Koh Y, Kim S, Lee SM, Kim HU, Ko JM, Lee SH, Yoon SS, Park S.
Genome Med
View full abstract on Pubmed
A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis.
Authors: Lee G, Lee SM, Kim HU.
Metab Eng
View full abstract on Pubmed
Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models.
Authors: Lee SM, Lee G, Kim HU.
Comput Struct Biotechnol J
View full abstract on Pubmed
Development of computational models using omics data for the identification of effective cancer metabolic biomarkers.
Authors: Lee SM, Kim HU.
Mol Omics
View full abstract on Pubmed