Congmin Zhu

HMI-Pred predicts gut microbe-host receptor interactions using deep learning, achieving high F1-scores and validating 47% of predictions through literature.

ESMARG, a large-language-model-based tool, improves antibiotic resistance gene detection in metagenomes and reveals seasonal dynamics and ecological factors in cyanobacterial blooms.

The paper introduces kLDM, a model that infers multiple metagenomic association networks under varying environmental conditions, showing improved performance over traditional methods.

The paper uses machine learning to assess gut microbiota-disease associations while accounting for human lifestyle factors, finding independent associations with IBD and unhealthy status but not others.