Next-Gen Medicine Lab(다음세대 의학연구실)

Joo Sang Lee

Artificial Intelligence, Data Science, Cancer Genomics, Cancer Immunotherapy Laboratory
다음세대 의학연구실
We are witnessing the beginning of a new generation of medicine. The unprecedented amount of large-scale next generation sequencing data from patients with cancer and other diseases makes data science and artificial intelligence (AI) play a pivotal role in biomedical research. The pressing question is how to best translate the accumulated data into a more effective practice of medicine. Next-Gen Medicine Lab aims to analyze the biological big-data with AI and data science approaches to find a better way to treat cancer. Our research covers highly translationally important questions in multiple fields of cancer biology and medicine with primary focus on precision cancer medicine, tailoring the cancer treatments based on the molecular markup of individual patients from the perspective of AI and data science.
R Keshet*, JS Lee*, et al. Targeting purine synthesis in ASS1 expressing tumors... Nature Cancer (in press).

S Kalaora*, JS Lee*, et al. Immunoproteasome expression is associated with better prognosis and response to checkpoint therapies in melanoma. Nature Communications (2020).

JS Lee, E Ruppin. Multiomics prediction of response rates to therapies to inhibit programmed cell death 1 and programmed cell death 1 ligand 1, JAMA Oncology (2019).

X Feng, N Arang, DC Rigiracciolo, JS Lee, et al. A platform of synthetic lethal gene interaction networks reveals that the GNAQ uveal melanoma oncogene controls the Hippo pathway through FAK. Cancer Cell (2019).

JS Lee*, L Adler*, et al. Urea cycle dysregulation generates clinically relevant genomic and biochemical signatures. Cell (2018).

JS Lee*, A Das*, et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nature Communications (2018).
(Tel) +82-31-299-6107,  (Email)