SUNGKYUNKWAN UNIVERSITY SCHOOL OF MEDICINE
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Hong-Hee Won
Hong-Hee Won PhD
| Graduate Program | Data Science and Personalized Medicine |
| Research Area | Multi-omics and AI |
| Laboratory | Genomics & Digital Health Lab Laboratory |
| wonhh@skku.edu | |
| Tel | +82-2-2148-7566 |
Education & Careers
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2020~Present Associate Professor, Sungkyunkwan University, Seoul, Korea
2020~Present Associate Professor, Samsung Medical Center, Seoul, Korea
2016~2020 Assistant Professor, Sungkyunkwan University, Seoul, Korea
2016~2020 Assistant Professor, Samsung Medical Center, Seoul, Korea
2012-2015 Research Fellow, Massachusetts General Hospital, Harvard Medical School, and Broad Institute of MIT and Harvard, Massachusetts, USA
2007-2011 PhD, Department of Bio and Brain Engineering, KAIST, Daejeon, Korea
2002-2004 MS, Department of Computer Science, Yonsei University, Seoul, Korea
1998-2002 BS, Department of Computer Science, Yonsei University, Seoul, Korea
Research Interest
Medical & population genomics
Since the Human Genome Project, genome-wide association studies (GWAS) have been successful in identifying common genetic variants that are associated with diverse human diseases or traits. However, only a small portion of heritability of the diseases or traits has been explained by the discovered common variants. Among many possible factors for missing heritability, rare variants with large effects are expected to make a major contribution to the missing heritability. Our lab identifies disease-causing genes and treatment targets by utilizing advanced genomic technologies (next-generation sequencing and statistical approaches for multi-omics data).
Digital health informatics
Future of medicine is summarized as P4 medicine, a discipline that is predictive, personalized, preventive and participatory. P4 medicine will improve healthcare substantially and enable precision medicine. Recent advances in mobile and wearable devices and genomic technology generate unprecedentedly huge and digitized health information. Not only academic but also industrial organizations are currently working on digital healthcare. Digital healthcare informatics encompass genetic, environmental and behavioral components of personal health. Genomic and clinical data collected in Samsung Medical Center can be also utilized. Our lab members with various expertise collaborate together towards this goal.
Machine learning, artificial intelligence & big data analysis
Data-driven medicine is a key aspect of future medicine. Machine learning techniques are very useful in generating data-driven hypotheses (ex., identifying unknown causal factors for disease) and prediction models of diagnosis and prognosis. In particular, deep neural network can be applied to unstructured data such as medical images, DNA sequences, etc. Our lab uses statistical and machine learning methods to address various biomedical questions.
Since the Human Genome Project, genome-wide association studies (GWAS) have been successful in identifying common genetic variants that are associated with diverse human diseases or traits. However, only a small portion of heritability of the diseases or traits has been explained by the discovered common variants. Among many possible factors for missing heritability, rare variants with large effects are expected to make a major contribution to the missing heritability. Our lab identifies disease-causing genes and treatment targets by utilizing advanced genomic technologies (next-generation sequencing and statistical approaches for multi-omics data).
Digital health informatics
Future of medicine is summarized as P4 medicine, a discipline that is predictive, personalized, preventive and participatory. P4 medicine will improve healthcare substantially and enable precision medicine. Recent advances in mobile and wearable devices and genomic technology generate unprecedentedly huge and digitized health information. Not only academic but also industrial organizations are currently working on digital healthcare. Digital healthcare informatics encompass genetic, environmental and behavioral components of personal health. Genomic and clinical data collected in Samsung Medical Center can be also utilized. Our lab members with various expertise collaborate together towards this goal.
Machine learning, artificial intelligence & big data analysis
Data-driven medicine is a key aspect of future medicine. Machine learning techniques are very useful in generating data-driven hypotheses (ex., identifying unknown causal factors for disease) and prediction models of diagnosis and prognosis. In particular, deep neural network can be applied to unstructured data such as medical images, DNA sequences, etc. Our lab uses statistical and machine learning methods to address various biomedical questions.
Representative Research Achievements
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1. Tissue-specific genetic features inform prediction of drug side effects in clinical trials. Sci Adv. 2020 6(37):eabb6242.
2. No causal effects of serum urate levels on the risk of chronic kidney disease: A Mendelian randomization study. PLoS Med. 2019 16(1):e1002725.
3. HMGCLL1 is a predictive biomarker for deep molecular response to imatinib therapy in chronic myeloid leukemia. Leukemia. 2019 33(6):1439-1450.
4. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature. 2017 544(7649):235-239.
5. Association of rare and common variation in the lipoprotein lipase gene with coronary artery disease. JAMA-J. Am. Med. Assoc. 2017 317(9):937-46.
6. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 536(7616):285-91.
7. Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease. N Engl J Med. 2016 374(12):1134-44.
8. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015 47(10):1121-30.
9. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature. 2015 518(7537):102-6.
10. Inactivating mutations in NPC1L1 and protection from coronary heart disease. N Engl J Med. 2014 371(22):2072-82.