Min Zeng

PROBind predicts, analyzes, and visualizes protein-protein and protein-nucleic acid binding residues using sequence and structure data, integrating multiple predictors and providing interactive tools.

Gra-CRC-miRTar is a pre-trained graph neural network framework for identifying miRNA targets in colorectal cancer, achieving better performance than existing methods and revealing functional interactions.

ViPal improves influenza virulence prediction by integrating prior viral knowledge into machine learning models using genomic sequences.

This paper introduces CDHGNN, a model using edge-weighted graph attention and heterogeneous graph neural networks to predict circRNA-disease associations, outperforming existing methods.

DeepLncLoc predicts lncRNA subcellular localization using a subsequence embedding method and text CNN, outperforming k-mer-based models and existing predictors.