Medical Science via Artificial Intelligence Lab
PI: Minji Jeon, PhD (전민지)
Department of Medicine
MedAI Lab at Korea University College of Medicine (PI: Minji Jeon, PhD) conducts medical science research via Artificial Intelligence (AI) using biomedical big data. The aims of our research are to develop machine learning / deep learning models that can contribute to better understanding about diseases and developing therapeutics for patients utilizing genomics or transcriptomics data. If you are interested in joining MedAI lab, please see Section Join us for more information.
AI-driven Drug discovery: Pain target identification (Biochemistry (2021)); Drug combination synergy prediction (Nature Communications (2019), BMC Systems Biology (2018)); Transcriptional response-based drug discovery (Bioinformatics (2019), PLoS Computational Biology (2021)); Drug-induced liver injury prediction (BigComp (2020)).
Biomedical Data Analysis: Jupyter Notebooks transformation into data-driven web-based applications (Patterns (2021)); Gene set enrichment analysis (Current Protocols (2021)); Co-expression-based gene function prediction (bioRxiv (2021)).
Network biomarker discovery: Context-specific subnetwork discovery (BMC Systems Biology (2018), PLoS One (2015)); Biomedical entity-relationship integration, visualization, and exploration (Bioinformatics (2013)).
Biomedical text mining: Knowledge discovery from biomedical literature (EMNLP(2021), Bioinformatics (2016), PLoS One (2016)); Biomedical text comprehension (JMIR medical informatics (2018)).
22-03-02 Dr. Minji Jeon joined Korea University College of Medicine as an assistant professor. The office is located at KU Mediscience Park.
22-07-18 Jitae and Minsu joined the lab as undergraduate students. Welcome!
22-07-25 An extensive protocols guide for effectively using the LINCS (Library of Integrated Network-based Cellular Signatures) resources has been just published in Current Protocols. Link
22-09-14 Songhyeon and Wootaek joined the lab as undergraduate students. Welcome!
22-09-15 A paper about a CycleGAN-based deep learning model that transforms L1000 profiles to RNA-seq-like profiles has been published in BMC Bioinformatics. Link