Liang He

ERNIE-RNA is an RNA language model that incorporates base-pairing restrictions to improve structural representation, achieving state-of-the-art performance in RNA structural and functional predictions.

DeepVirus identifies and classifies viral dark matter in metagenomic data using a hierarchical transformer framework, revealing novel viral lineages and expanding known RNA virus diversity.

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

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