Role: Research Fellow, ANU Neuroinformatics Group, with collaborators across the ANU School of Medicine & Psychology.

This study evaluates machine-learning approaches for identifying and disentangling blood-based markers associated with multiple sclerosis, working toward earlier and less-invasive detection.

Outputs

• Disentangling Blood-Based Markers of Multiple Sclerosis Through Machine Learning: An Evaluation Study — medRxiv preprint
• Vlieger, R., Rizia, M. M., Amjadipour, A., Cherbuin, N., Brüstle, A., & Suominen, H. — MedInfo 2025 (IOS Press, Studies in Health Technology and Informatics)

Methods compared: A mix of ML & DL models