Genomics · Precision Medicine
Decoding the genome to bridge biology and the clinic - turning genome
big data into precision-medicine insight for individual patients.
ABOUT THE LAB
The era of genome big data and precision medicine
Recent advances in genome profiling techniques such as high-throughput next-generation sequencing have advanced our understanding of biology across diverse research fields including genetics and genomics. With this technical breakthrough, genomics data is being accumulated at an unprecedented rate and scale - becoming one of the biggest datasets humanity has ever seen. This is the era of genome big data and precision medicine, where advanced technologies and knowledge can guide diagnostic and therapeutic strategies for individual patients.
With our lab members and collaborators in clinical and experimental labs, we investigate the genomics of various tumor and non-tumor diseases, searching for biologically and clinically relevant findings and markers for patients.
AIM 01
Use
Apply advanced methodologies to learn the unique genomic -transcriptomic - epigenomic configuration of normal and disease genomes.
AIM 02
Translate
Translate knowledge into the clinic with our collaborators - identifying biomarkers and building models to predict prognosis, treatment response, or resistance.
AIM 03
Develop
Develop novel informatics algorithms and methodology to push the boundaries of computational genomics.
RESEARCH
Research areas
Cancer genomics
Base pair–resolution comparison of cancer and matched normal genomes reveals alterations driving disease initiation and progression. Over decades we've analyzed lung cancer, CML, hepatocellular carcinoma, cholangiocarcinoma, and melanoma — focusing on the clinical translation of genomic insight to identify clinically relevant biomarkers.
PanCancer analysis & public datasets
Global efforts such as TCGA (~10,000 cancers) and ICGC (~3,000 cancers) have produced high-quality multiomics datasets available to the research community. We leverage these large-scale public resources for integrative pan-cancer analyses across tumor types.
People
Principal
Tae Min Kim, MD, PhD
Professor · Dept. of Medical Informatics
CONTACT
tmkim@catholic.ac.kr
02-3147-8426 (office)
AREA OF RESEARCH
Genomics · Oncology
EDUCATION & APPOINTMENTS
2017 – Present
2003 – 2009
1994 – 2000
Professor, Dept. of Medical Informatics, The Catholic University of Korea
PhD in Microbiology, The Catholic University of Korea
MD in Medicine, College of Medicine, Seoul, The Catholic University of Korea
PUBLICATIONS
Selected recent work
2026
Sunmin Kim, Seeyoun Lee, Hyeji Kim, … Tae-Min Kim and Sun-Young Kong. Delineation of the heterogeneity underlying genomic instability in hereditary breast cancers reveals four disease subtypes. Experimental & Molecular Medicine.
2025
Ji-Won Chun, Jae-Chang Kim, … Tae-Min Kim, Dai-Jin Kim. The role of the salience network in adolescent impulsivity: insights from memory tasks and neuroimaging. Research Square.
Tae Hoon Kim, Dagyeong Lee, … Tae-Min Kim, … Hoon Hur. Cancer-associated fibroblast–derived GAS6 increases resistance to chemotherapy through AXL/STAT3/ABCG1 in gastric cancer. British Journal of Cancer.
Seo-Young Lee, Yun-Hee Lee, Tae-Min Kim and U.-Syn Ha. TGF-β signaling and tumor microenvironment dynamics in bladder cancer progression post-BCG therapy: a longitudinal single-nucleus RNA-seq study. BMC Cancer.
Youngbeen Moon, Young-Ho Kim, Jong-Kwang Kim, Chung Hwan Hong, Eun-Kyung Kang, Hye Won Choi, Dong-eun Lee,
Tae-Min Kim, Seong Gu Heo, Namshik Han and Kyeong-Man Hong, Evaluation of false positive and false negative errors in targeted next generation sequencing, Genome Biology
CONTACT
Visit the lab
Department of Medical Informatics
222, Banpo-daero, Seocho-gu,
Seoul, Republic of Korea