Duolin Wang

This paper introduces DPLM, a dynamics-aware protein language model that integrates molecular dynamics simulations with sequence data to improve predictions of protein properties.

This study introduces ProtLoc-GRPO, a reinforcement learning method that improves subcellular localization prediction by optimizing PPI networks, achieving a 7% macro-F1 score improvement.

The paper introduces two sequence-based models to predict kinase-specific phosphorylation sites, showing improved performance and zero-shot capability.

This paper introduces S-PLM, a 3D structure-aware protein language model that improves prediction tasks by integrating structural information through efficient fine-tuning.

S-PLM improves protein language models by integrating 3D structure information through contrastive learning, outperforming sequence-only models in clustering and classification tasks.

PEFT-SP improves signal peptide prediction using parameter-efficient fine-tuning on large protein language models, achieving higher MCC2 gains with limited training data.

Prot2Token is a multi-task framework that uses autoregressive language modeling for protein prediction tasks, achieving performance comparable to specialized models and enabling integration with large language models.

DeepMAPS infers biological networks from single-cell multi-omics data using a heterogeneous graph transformer, outperforming existing tools in cell clustering and pathway prediction.