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Y. (Yunlei) Li, Assistant Professor

Principal Investigator

  • Department
  • Pathology & Clinical Bioinformatics



Dr. Yunlei Li studied data mining & bioinformatics at Delft University of Technology (TUD) under the supervision of Prof. Marcel Reinders and Prof. Lodewijk Wessels. She conducted her internship at the Netherlands Cancer Institute (NKI) under the supervision of Prof. Laura van ‘t Veer on breast cancer research. In 2004, she obtained her MSc degree with honor (cum laude). She performed her doctoral research at TUD with Prof. Marcel Reinders and Prof. Dick de Ridder and obtained her PhD in 2010 on “Exploiting noisy and incomplete biological data for prediction and knowledge discovery”. Since 2011, Dr. Li has been working at the Erasmus Medical Center (EMC) on multiple basic cancer research as well as health care-oriented projects covering a wide range of diseases (incl. leukemia, aneurysm, pancreatic cancer, lung cancer, glioblastoma, melanoma) and bioinformatics applications.


Currently she is leading the machine learning / artificial intelligence research line in the Department of Pathology & Clinical Bioinformatics at EMC. Her research focus is designing and applying novel artificial intelligence methods (data mining, machine learning, deep learning) in translational and clinical research settings to integrate and exploit multi-dimensional patient data (e.g. DNA, RNA, protein, image, clinical) for clinical cancer research and clinical trials, in order to gain insight of the diseases, discover biomarkers, improve diagnosis accuracies and stratification for personalized treatments.


Fundings & grants

  • EU FP7: TTT - Tailored Antimicrobial Treatment: Patient stratification to reduce antibiotic use
  • EU EUROSTARS: iKnowIT - Integrated knowledge discovery IT: Clinical Decision Support platform for Pancreatic Cancer
  • EU H2020 & MSCA ITN: GlioTrain: Exploiting GLIOblastoma intractability to address European research TRAINing needs in translational brain tumour research, cancer systems medicine and integrative multi-omics
  • EU H2020: CINECA: Common Infrastructure for National Cohorts in Europe, Canada, and Africa
  • ESPID: Local host response defines patient clusters that reveal the pneumonia-causing pathogen
  • Kika: Detection of novel mutations and deregulated signaling pathways in T-cell acute lymphoblastic leukemia
  • KWF: IMPROVE study: Integration of clinical data, Multi-omics and pathomics by artificial intelligence to imPROVe prognostic prediction of Early stage melanoma
  • Hanarth: Artificial Intelligence-Driven Clinical Decision Support for Pancreatic Cancer
  • Hanarth: Histogenomic biomarker identification to improve neuroendocrine lung tumor diagnostics on biopsies, using multiplex immunohistochemistry and artificial intelligence assisted histomorphological classification
  • ZonMW ETH: PancCanNet: A knowledge resource for EU pancreatic cancer translational research projects
  • NWO Corona: Immune monitoring in COVID-19 patients
  • Support Casper: The role of immune system in pancreatic cancer
  • Stichting Lever Onderzoek: LAPC-2: Feasibility and efficacy of the addition of IMM-101 to standard stereotactic radiotherapy in locally advanced pancreatic cancer patients
  • Hartwig Foundation: Developing Personalised Treatment Strategies Driven by the Genomic Landscape of Metastatic Pancreatic Cancer