Kieran D. Lamb

This paper introduces EvoMIL, a deep learning method that predicts virus-host associations using protein language models and multiple instance learning, achieving improved accuracy in both prokaryotic and eukaryotic hosts.

PLM-interact improves PPI prediction by jointly encoding protein pairs, achieving 16-28% AUPR gains across multiple species.

This paper shows that protein language models can assess mutation effects in SARS-CoV-2 spike protein without multiple sequence alignments, capturing evolutionary constraints and variant properties.

The paper uses machine learning to identify mutational signatures in SARS-CoV-2 genomes and links them to host antiviral molecules, showing these drive most of the virus's evolutionary changes.