An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction

Kyudam Choi, Yurim Lee, Cheongwon Kim

Keywords: Bioinformatics, Protein function prediction, Machine Learning

Abstract: 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.

One-sentence Summary: We introduce a novel protein function model combined pre-trained language model and an efficient GCN-based model.