Daniel Ritter

LOL-EVE predicts promoter variant effects using a transformer model trained on mammalian promoter sequences, showing improved performance in indel prediction and haplotype analysis.

ProteinGym unifies over 250 deep mutational scans and ~65k clinical variants to benchmark over 70 models in zero-shot and supervised settings; TranceptEVE ranks best overall.

Blending an alignment-based family model with an autoregressive protein language model, weighted by how deep the alignment is, reportedly improves mutation-effect prediction against experimental assays and ClinVar.