29/05/2026
PART A of the COST Action NEUTRO-NARPS Training School "Machine Learning Approaches for Hematological Disorder Diagnostics and Prognostics" has been successfully completed!
Over two days of interactive learning, participants explored how Machine Learning can transform the diagnosis, prognosis, and treatment stratification of hematological disorders through the analysis of clinical, genomic, and biomedical data.
We were honored to host:
🔹 Dr Gabriel Alexander Vignolle (UCLA) presented the foundations of Machine Learning in Hematology, covering the complete ML workflow from data preprocessing and feature engineering to model selection, interpretation of predictive models in clinical practice, and ethical considerations in AI-driven healthcare.
🔹 Dr Andrea Cappozzo (Università Cattolica del Sacro Cuore, Milan) introduced advanced methodologies for analysing complex biomedical data, including supervised and unsupervised learning, mixed-effects modelling for multicentric studies, penalized estimation techniques for high-dimensional datasets, and synthetic applications in DNA methylation biomarker development.
🔹 Dr. Georgios Manikis (Hellenic Mediterranean University), who led a hands-on training session, providing participants with practical experience of different machine learning methodological scenarios using synthetic datasets in the context of KNIME.
A heartfelt thank you to all presenters, trainers, and participants for making this event a success. Special thanks to the WG3 leadership team for their dedication and excellent organization.
Together, we continue strengthening research capacity and fostering innovation in rare blood disease diagnostics and prognostics.
More information: https://neutro-narps.eu/ml-hematology/