Cong Feng

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

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.

This study benchmarks APA detection tools in single-cell and spatial transcriptomes, finding SCAPE and scAPAtrap perform best across multiple metrics.