A moving target: non-stationary selection governs unsupervised prediction of viral fitness
Anticipating how mutations change viral fitness is central to genomic surveillance and vaccine design, yet the supervised phenotype data behind the most accurate variant-effect predictors are unavailable for most emerging pathogens. We ask how far label-free scoring can go using only sequences, their evolutionary history, and structure. We assemble a modular, fully unsupervised pipeline that…
We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.
