Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO

Efficient computational methods for protein functional annotation help bridge the gap between high-throughput sequence data and unknown protein functions. While many data-driven methods predict protein functions based on protein-level information, they often overlook the relationships between different functions. In this work, we introduce PFresGO, an attention-based deep learning approach that utilizes the hierarchical structure of gene ontology (GO) graphs to predict multiple protein functions in a high-throughput manner. PFresGO is available for academic use at https://github.com/BioColLab/PFresGO . We provide an overview of our predictor, discuss its ability to accurately predict protein functions, and demonstrate how to interpret the results.