David Belanger

This study combines machine learning and high-throughput screening to design improved nuclease enzymes, finding a more effective variant than standard directed evolution methods.

ProtEx improves protein function prediction by using retrieved exemplars, achieving state-of-the-art results for EC numbers, GO terms, and Pfam families.

ProteInfer uses deep convolutional neural networks to predict protein functions from amino acid sequences, offering efficient and accurate predictions with a lightweight software interface.

This paper introduces SLIP, a framework for creating synthetic protein fitness landscapes to benchmark ML-guided protein design pipelines.

This paper uses deep learning to improve protein function annotation by training on Pfam data, achieving better accuracy and efficiency than BLASTp.