Next-Gen
Medicine Lab
Department of Artificial Intelligence &
School of Medicine,
Sungkyunkwan University
Opening the era of next generation of medicine
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 (NGML) aims to integrate the biological big-data with AI and data science approaches and cutting-edge experimental techniques to find a better way to treat cancer patients. Our research covers highly translationally important questions in multiple fields of cancer biology including cancer immunotherapy. In particular, we 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.
Professor
Joo Sang Lee
Department of Artificial Intelligence
Department of Precision Medicine
School of Medicine
Sungkyunkwan University
EDUCATION AND TRAINING
2018-2019
National Cancer Institute
Cancer Data Science Lab
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2014-2018
University of Maryland
Center for Bioinformatics and Computational Biology
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2006-2012
Northwestern University
PhD in Physics
RESEARCH INTEREST
Artificial Intelligence and Data Science
Analyze biological big data to provide clinically relevant novel insights.
Precision Medicine
Develop statistical algorithms to match right patients to the right drugs.
Cancer Immunotherapy
Identify new opportunities to harness immune systems to fight against cancer.
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Cancer Metabolism
Metabolic dysregulation leads to cancer mutagenesis and new opportunities for treatment.
Publications
Cell 2021
JS Lee# et al.
Synthetic lethality mediated precision oncology via the tumor transcriptome
Nature Commun.
2021
S Sinha, ..., JS Lee#, et al.
A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing
Nature Cancer 2020
R Keshet*, JS Lee*, et al.
Targeting purine synthesis in ASS1-expressing tumors enhances the response to immune checkpoint inhibitors
Nature Commun.
2020
S Kalaora*, JS Lee*, et al.
Immunoproteasome expression is associated with better prognosis and response to checkpoint therapies in melanoma
Nature Cell Biol. 2019
G Pathria, JS Lee, et al.
Translational reprogramming marks adaptation to asparagine restriction in cancer
JAMA Onc.
2019
JS Lee#, E Ruppin
Multiomics prediction of response rates to therapies to inhibit programmed cell death 1 and programmed cell death 1 ligand 1
Cancer Cell
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
Nature Med.
2018
N Auslander, G Zhang, JS Lee, DT Frederick, B Miao, T Moll, T Tian, et al.
Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma
Cell
2018
JS Lee, et al.
Urea cycle dysregulation generates clinically relevant genomic and biochemical signatures
Nature Commun.
2018
JS Lee, et al. Harnessing synthetic lethality to predict the response to cancer treatment
Affiliations
Contact
FULL ADDRESS:
Positions are open for all levels. Please contact us via the following form.