Steffan B. Paul

The paper develops a synthetic Fab yeast display library integrated with machine learning to discover antibodies against cell surface antigens, yielding hundreds of validated antibodies and a public dataset.

This paper uses machine learning to predict high-affinity nanobody binders from sparse sequencing data, outperforming deep learning and semi-supervised methods with linear models.

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.

This paper introduces Global Importance Analysis (GIA), a method to quantify the effect size of genomic features in deep neural networks, demonstrating its application in predicting RNA-protein interactions.

This paper introduces ResidualBind, a convolutional residual network that infers RNA-binding protein sequence and structure specificities from enriched sequence data, outperforming previous methods and learning RNA structure context from sequence data.