Yuqing Yang

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

The paper identifies a microenvironment-driven neoantigen, C19S, in type 1 diabetes that activates persistent memory T cells through HLA-DQ8 recognition.

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

This study examines gene duplication and divergence in GH50 agarases from Agarivorans ablus, showing structural and functional divergence while maintaining conserved backbone structures.

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.

The paper identifies six differentially expressed genes in glioblastoma multiforme, highlighting FPR2 as a potential immunotherapeutic target.

The paper introduces mLDM, a hierarchical Bayesian model to discover microbial associations in metagenomic data while addressing compositional bias and environmental factors.