Dokyoon Kim, PhD
My research entails the development and application of data integration approaches to improve the ability to diagnose, treat, and prevent complex diseases. My primary focus lies in integrating multi-omics data and biological knowledge to better translate genomic and biomedical data from electronic health records (EHR) into clinical products. My past projects have been both theoretical and applied, and they include developing data integration methods that combine multi-omics data and biological knowledge, predicting cancer clinical outcomes based on interactions between multi-omic features, and identifying gene-by-environment (GxE) interactions in several phenotypes/diseases. I plan to continue my work in these areas, focusing primarily on providing actionable clinical products based on inter-plays within/between different dimensional genomic data. In particular, my long-term research goal is to develop and evaluate sophisticated data integration methods that simultaneously combine peoples’ individual variations in genomic (‘omic) data, phenotype data from EHR, and environment/lifelog data for improved precision medicine.
- Kim D, Lucas A, Glessner J, Verma SS, Bradford Y, Li R, Frase AT, Hakonarson H, McCarty CA, Peissig P, Brilliant M, Ritchie MD. (2016, Jan). Biofilter as a functional annotation pipeline for common and rare copy number burden. Pacific Symposium on Biocomputing (PSB), 21:357-368.
- Kim D, Li R, Dudek SM, Ritchie MD. (2015, Aug). Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer. J Biomed Inform , 56, 220-228.
- Kim D, Joung JG, Sohn KA, Shin H, Ritchie MD, Kim JH. (2015, Jan). Knowledge Boosting: A graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J Am Med Inform Assoc , 22(1):109-120.
- Kim D, Li R, Lucas A, Verma SS, Dudek S, Ritchie MD. (2016, Dec). Using knowledge-driven genomic interactions for multi-omics data analysis: meta-dimensional models for predicting clinical outcomes in ovarian carcinoma. J Am Med Inform Assoc. Full Text
- Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nature Reviews Genetics , 16, 85-97.
EducationPhD, Biomedical Informatics, Seoul National University, 2013
Postdoctoral Training, Pennsylvania State Univeristy, 2013-2016