Licheng Zong

The paper shows that biological meaning in protein embeddings depends on resolution, with different levels of organisation preserving distinct biological relationships.

AcrNET predicts anti-CRISPR proteins using a deep learning model, achieving significant performance improvements and class-level predictions.

CLEAR is a self-supervised contrastive learning framework for scRNA-seq that improves clustering, batch correction, and pseudo-time inference by reducing technical noise and heterogeneity.

A BERT-style model pretrained on 23.7 million unlabelled ncRNA sequences gives embeddings that improve secondary structure, contact, distance-map and some function predictions over task-specific and MSA-based baselines.

This study introduces HMD-AMP, a deep learning framework for multi-label classification of antimicrobial peptides, improving performance on minor classes and handling multiple targets.