Tiziana Sanavia

The paper introduces ProbJanusDDG to prioritize mutations for experimental follow-up by using conformal prediction intervals to assess uncertainty in predicted stability changes.

This paper shows that biologically informed neural networks (BINNs) cannot reliably recover internal biological structures from outputs alone due to nonidentifiability.

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