Yanyan Zhu

HPInet: Interpretable prediction of Host-Pathogen protein-protein Interactions using a transformer-based neural network

HPInet predicts host-pathogen protein-protein interactions using a transformer-based model, achieving high accuracy and identifying key residues from sequence data.

Improved Prediction of Bacterial Type VI Secretion Effector Proteins Using an Integrated Convolutional Neural Network Model Combining N-terminal Signal Sequences, Evolutionary Information and Pre-Trained Protein Language Features

This paper introduces T6CNN, a model that improves T6SE prediction by integrating signal sequences, evolutionary data, and pre-trained features, achieving higher accuracy, sensitivity, and specificity than existing tools.

T4SEpp: a pipeline integrated with protein language models effectively predicting bacterial type IV secreted effectors

T4SEpp predicts bacterial type IV secreted effectors using protein language models and integrates multiple modules, achieving high sensitivity and specificity.

Identification of disease resistance genes from a Chinese wild grapevine (Vitis davidii) by analysing grape transcriptomes and transgenic Arabidopsis

The paper identifies disease resistance genes in Chinese wild grapevine by comparing transcriptomes and validates one gene's role in enhancing resistance in Arabidopsis.