Nicholas Furnham

NoroScope: Exploring the Mutational Landscape of the Human Norovirus Capsid Protein with Context-Aware Machine Learning

NoroScope uses context-aware machine learning to prioritize amino-acid substitutions in the norovirus capsid protein, achieving high precision in predicting mutation effects with limited data.

Risk Based Prediction of Novel AMR Variants Using Protein Language Models

The paper introduces AMRscope, a protein language model-based tool for predicting antimicrobial resistance likelihood from single mutations, showing strong performance across bacterial species and identifying new resistance candidates.

Allosteric Site Prediction Using Protein Language Models and Orthosteric Conditioning

The paper predicts allosteric binding sites using protein language models with orthosteric conditioning, outperforming static embeddings and achieving results comparable to structure-based methods.