An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction
This addresses protein function prediction for bioinformatics, but it appears incremental as it builds on existing GCN and language model approaches.
The paper tackled protein function prediction by developing a method that uses a language model for protein sequences and a graph convolutional network for hierarchical Gene Ontology terms, achieving state-of-the-art performance.
We propose an effective method to improve Protein Function Prediction (PFP) utilizing hierarchical features of Gene Ontology (GO) terms. Our method consists of a language model for encoding the protein sequence and a Graph Convolutional Network (GCN) for representing GO terms. To reflect the hierarchical structure of GO to GCN, we employ node(GO term)-wise representations containing the whole hierarchical information. Our algorithm shows effectiveness in a large-scale graph by expanding the GO graph compared to previous models. Experimental results show that our method outperformed state-of-the-art PFP approaches.