Y. (Yunlei) Li, Assistant Professor

Dr.ir. Yunlei Li is an Assistant Professor and Principal Investigator in the Department of Pathology & Clinical Bioinformatics at Erasmus MC. She was trained in Machine Learning and Bioinformatics at Delft University of Technology, where she graduated cum laude in 2004. Her MSc thesis was conducted at the Netherlands Cancer Institute (NKI), focusing on breast cancer genomics. She subsequently obtained her PhD from Delft University of Technology on computational methods for prediction and knowledge discovery from complex biological data.

Since joining Erasmus MC in 2011, she has led and contributed to interdisciplinary research projects in oncology, molecular diagnostics, infectious diseases, respiratory medicine and clinical bioinformatics. Her work combines artificial intelligence with diverse biomedical and healthcare data, including molecular, imaging, laboratory and clinical data, for biomarker discovery, patient stratification, disease prognosis and precision medicine across multiple disease areas.

Her current research focuses on developing AI methods that transform heterogeneous routine healthcare and diagnostic data into actionable clinical intelligence for healthcare professionals throughout the healthcare pathway. Her group develops and evaluates AI approaches that identify clinically relevant patterns from electronic health records, molecular diagnostics, laboratory measurements, imaging data, and patient-generated health data. Within the Erasmus MC Diagnostic 2030 programme, she works on AI-driven approaches for integrated diagnostics, clinical decision support, diagnostic workflow optimisation, and data-driven healthcare innovation. Current application areas include infectious disease screening, toxicology screening, early disease detection, molecular diagnostics, prognostic assessment, treatment decision support, and longitudinal patient monitoring.

She collaborates closely with clinicians, diagnostic laboratories, healthcare organisations, AI researchers, and international partners to translate AI innovations into practical solutions that improve healthcare quality, efficiency, and sustainability.

• WEEPI (Western-Eastern European Partnership Initiative on HIV, Viral Hepatitis and TB): Aware.Hep -- Fostering early and equitable viral hepatitis diagnosis through a streamlined clinical and laboratory framework in Eastern Europe
• EADV (European Academy of Dermatology and Venereology): IMPROVE-TIME -- Improve prognostic prediction of early-stage melanoma with (spatial) multi-omics analyses of the tumor-immune microenvironment
• ESPID (European Society for Paediatric Infectious Diseases): Local host response defines patient clusters that reveal the pneumonia-causing pathogen
• 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
• ErasSupport: AI4HIV -- Develop and validate multilingual natural language processing models to process electronic health records and assist medical professionals with efficient HIV screening and case identification
• Hanarth Foundation: D-ESMEL TME -- AI-Driven exploration of the melanoma tumor microenvironment in the Dutch Early Stage Melanoma (D-ESMEL) study
• Hanarth Foundation: pNET -- Histogenomic biomarker identification to improve neuroendocrine lung tumor diagnostics on biopsies, using multiplex immunohistochemistry and artificial intelligence-assisted histomorphological classification
• KWF (Dutch Cancer Society): IMPROVE -- Integration of clinical data, multi-omics and pathomics by artificial intelligence to improve prognostic prediction of early-stage melanoma
• Hanarth Foundation: Response prediction to neoadjuvant chemotherapy in patients with triple negative breast cancer based on integrated diagnostics
• Hanarth Foundation: Artificial intelligence-driven clinical decision support for pancreatic cancer
• Hanarth Foundation: 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
• Kika (Dutch Children Cancer-Free Foundation): Detection of novel mutations and deregulated signaling pathways in T-cell acute lymphoblastic leukemia