Shuai Zeng

This paper introduces STORM, a statistical framework for analyzing spatial patterns in multi-omics data, demonstrating reliable performance in detecting spatial structures and supporting study design decisions.

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.

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

G2PDeep-v2 is a web-based deep learning platform for phenotype prediction and biomarker discovery using multi-omics data across organisms, offering model training and visualization tools.

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