Corrado Pancotti

This paper introduces a deep generative decoder model for dimensionality reduction with millions of features and few samples, showing it outperforms PCA and VAE in tumor classification.

This paper identifies flaws in current Drug Response Prediction evaluation methods and proposes new strategies to improve their reliability and generalization.

MUSE-XAE extracts mutational signatures from cancer genomes using an explainable Auto-Encoder, achieving accurate extraction and improving tumour-type classification.