Giuseppe Di Fede

The paper identifies the CSF YWHAG:NPTX2 ratio as a biomarker for predicting clinical severity and phenoconversion in FTLD, showing strong correlation with disease progression and improved trial design potential.

This paper uses machine learning to stratify genetic frontotemporal dementia patients into subtypes and provides progression scores that improve trial enrichment efficiency.

This paper predicts Alzheimer's disease progression from sparse multimodal clinical data using NeuralODE models, showing improved accuracy over existing methods.