Lijun Wu

Accelerating protein engineering with fitness landscape modeling and reinforcement learning

The paper introduces {micro}Protein, a framework combining deep learning and reinforcement learning to predict and discover high-gain protein mutants using single mutation data.

CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms

CREaTor models cis-regulatory patterns using attention mechanisms and RNA-seq/ChIP-seq data, outperforming existing methods in predicting cCRE-gene interactions across cell types.

Retrieved Sequence Augmentation for Protein Representation Learning

This paper introduces a method to improve protein representation learning by retrieving and integrating similar sequences without alignment, achieving faster and more accurate predictions than traditional methods.

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design

This paper develops a pre-trained antibody language model to improve CDR sequence and structure generation, outperforming previous methods in antigen-specific antibody design.