Laboratory of Veterinary Informatics & Bioinformatics

Veterinary Pathobiology and Preventive Medicine

Laboratory of Veterinary Informatics & Bioinformatics

Veterinary Informatics is a field that utilizes data science and artificial intelligence technologies to address various issues in modern veterinary medicine. This discipline deals with managing and analyzing vast amounts of data generated in animal health, disease management, and life science research to extract useful information. Data-driven research plays a crucial role in developing more sophisticated diagnostic methods, personalized treatments, and preventive strategies. Recently, research utilizing large language models (LLMs) has also been actively conducted. Data analysis through LLMs aids in recognizing complex disease patterns and improving predictive models, and the introduction of such technologies is expected to bring innovative changes to the field of veterinary medicine.
Our laboratory conducts research in the following areas:
(1) Machine learning analysis of clinical data and lifelog data from the general public.
(2) Study of variants affecting gene splicing and the interaction between epigenetics and splicing.
(3) Development of LLMs for pet health consultations for companions.
(4) Development of laboratory information management systems specialized for preclinical trials for infectious diseases and a preclinical data visualization portal.

Faculty

Lee, Younghee

○ Address: Rm #631, College of Veterinary Medicine (Bldg #85), Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826
○ Phone: +82-2-880-1247
○ Homepage: https://genomics.snu.ac.kr
○ E-mail: amazon@snu.ac.kr

Courses

Undergraduate
• Veterinary Statistics
• Veterinary Informatics
• Innovation and Entrepreneurship for Veterinary Students 1, 2

Graduate
• Veterinary Data Analysis Methodology (Bioinformatics Analysis Methods and Coding Practice)
• Veterinary Data Analysis Methodology (Current Trends in Biomedical Information Analysis Techniques)

Recent Publications

  • Kim, Yeongmin et al. “A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records.” BioData mining vol. 17,1 14. 25 May. 2024, doi:10.1186/s13040-024-00366-0
  • Chamberlin, John T et al. “Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments.” Genome research vol. 34,2 179-188. 20 Mar. 2024, doi:10.1101/gr.278253.123
  • Moutinho, Miguel et al. “TREM2 splice isoforms generate soluble TREM2 species that disrupt long-term potentiation.” Genome medicine vol. 15,1 11. 20 Feb. 2023, doi:10.1186/s13073-023-01160-z
  • Kim, Sara et al. “Brain Region-Dependent Alternative Splicing of Alzheimer Disease (AD)-Risk Genes Is Associated With Neuropathological Features in AD.” International neurourology journal vol. 26,Suppl 2 (2022): S126-136. doi:10.5213/inj.2244258.129
  • Shivakumar, Manu et al. “Epigenetic interplay between methylation and miRNA in bladder cancer: focus on isoform expression.” BMC genomics vol. 22,Suppl 3 754. 21 Oct. 2021, doi:10.1186/s12864-021-08052-9
  • Han, Seonggyun et al. “Alternative Splicing Regulation of Low-Frequency Genetic Variants in Exon 2 of TREM2 in Alzheimer’s Disease by Splicing-Based Aggregation.” International journal of molecular sciences vol. 22,18 9865. 13 Sep. 2021, doi:10.3390/ijms22189865
  • Han, Seonggyun et al. “ADAS-viewer: web-based application for integrative analysis of multi-omics data in Alzheimer’s disease.” NPJ systems biology and applications vol. 7,1 18. 19 Mar. 2021, doi:10.1038/s41540-021-00177-7
  • Jin, Young-Joo et al. “Differential alternative splicing between hepatocellular carcinoma with normal and elevated serum alpha-fetoprotein.” BMC medical genomics vol. 13,Suppl 11 194. 28 Dec. 2020, doi:10.1186/s12920-020-00836-4
  • Jung, Hyeim et al. “Transcriptome analysis of alternative splicing in the pathogen life cycle in human foreskin fibroblasts infected with Trypanosoma cruzi.” Scientific reports vol. 10,1 17481. 15 Oct. 2020, doi:10.1038/s41598-020-74540-9
  • Han, Seonggyun et al. “Alternative Splicing Regulation of an Alzheimer’s Risk Variant in CLU.” International journal of molecular sciences vol. 21,19 7079. 25 Sep. 2020, doi:10.3390/ijms21197079
  • Kweon, Solbi et al. “Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study.” Journal of medical Internet research vol. 22,8 e18387. 10 Aug. 2020, doi:10.2196/18387
  • Tashjian, Robert Z et al. “Gene Expression in Torn Rotator Cuff Tendons Determined by RNA Sequencing.” Orthopaedic journal of sports medicine vol. 8,6 2325967120927480. 29 Jun. 2020, doi:10.1177/2325967120927480