Yingheng Wang

AIMe uses neuro-symbolic AI to predict and annotate small-molecule spectra, expanding the searchable chemical space by three orders of magnitude.

This paper introduces LC-PLM, a protein language model based on BiMamba-S architecture, showing improved performance on protein tasks compared to Transformer-based models.

CAT Bridge identifies gene-metabolite associations using time-series multi-omics data and benchmarks statistical methods for longitudinal analysis.

This paper introduces MolCLE, a molecular graph contrastive learning framework with parameterized explainable augmentations, which outperforms baselines on real-world datasets with fewer parameters.