Staff Fellow — Division of Bioinformatics and Biostatistics
Jae Hyun Kim, Ph.D.
Dr. Jae Hyun Kim received a Ph.D. in electrical engineering and computer science from Korea Advanced Institute of Science and Technology, South Korea, and received a second Ph.D. in bioengineering from the University of Kansas in Lawrence, Kansas. He joined the Division of Bioinformatics and Biostatics at the National Center for Toxicological Research (NCTR) in 2016.
Dr. Kim fills several roles at NCTR. He serves as the technical lead on multiple projects in collaboration with other FDA centers to develop systems such as Investigational New Drugs (IND) Smart Template Systems and Data Analysis Search Host (DASH). His work was recognized for its high standard of quality, earning awards including FDA Scientific Achievement Awards (Outstanding Intercenter Scientific Collaboration—The Smart Template System Group) and the FDA Amendments Act Innovator’s Award (DASH Team).
Dr. Kim’s research background is in software system design and full-stack development. His primary research interest lies in natural language processing (NLP) and its application in the field of regulatory science. He is exploring how NLP and machine learning can be used to make the work of regulatory scientists more effective. By automating the process of extracting information from regulatory documents, Dr. Kim aims to reduce the time and effort required by regulatory scientists to obtain and analyze information.
CYPminer: An Automated Cytochrome P450 Identification, Classification, and Data Analysis Tool for Genome Data Sets Across Kingdoms.
Kweon O., Kim S.J., Kim J.H., Nho S.W., Bae D., Chon J., Hart M., Baek D.H., Kim Y.C., Wang W., Kim S.K., Sutherland J.B., and Cerniglia C.E.
BMC Bioinformatics. 2020, 21(1):160. doi: 10.1186/s12859-020-3473-2.
Characterization and Protective Efficacy of Type Three Secretion Proteins as a Broadly Protective Subunit Vaccine Against Salmonella enterica Serovars.
Martinez-Becerra F.J., Kumar P., Vishwakarma V., Kim J.H., Arizmendi O., Middaugh C.R., Picking W.D., and Picking W.L.
Infect Immun. 2018, 86(3). pii: IAI.00473-17. doi: 10.1128/IAI.00473-17.
Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches with Analytical Stability Data Sets.
Nariya M.K., Kim J.H., Xiong J., Kleindl P.A., Hewarathna A., Fisher A.C., Joshi S.B., Schöneich C., Forrest M.L., Middaugh C.R., Volkin D.B., and Deeds E.J.
J Pharm Sci. 2017, 106(11):3270-3279.
Improved Comparative Signature Diagrams (CSDs) to Evaluate Similarity of Storage Stability Profiles of Different IgG1 mAbs.
Kim J.H., Joshi S.B., Esfandiary R., Iyer V., Bishop S.M., Volkin D.B., and Middaugh C.R.
J Pharm Sci. 2016, 105(3):1028-35.
Biosimilarity Assessments of Model IgG1-Fc Glycoforms Using a Machine Learning Approach.
Kim J.H., Joshi S.B., Tolbert T., Middaugh C.R., Volkin D.B., and Smalter Hall A.M.
J Pharm Sci. 2016, 105(2):602-12.
Correlating the Impact of Well-Defined Oligosaccharide Structures on Physical Stability Profiles of IgG1-Fc Glycoforms.
More A.S., Toprani V.M., Okbazghi S.Z., Kim J.H., Joshi S.B., Middaugh C.R., Tolbert T.J., and Volkin D.B.
J Pharm Sci. 2016, 105(2):588-601.
- Contact Information
- Jae Hyun Kim
- (870) 543-7121
ExpertiseApproachDomainTechnology & DisciplineToxicology