Multi-view attention graph convolutional networks for the host prediction of phages

Abstract

Phages play pivotal roles in various biological processes, and the study of host prediction of phages (HPP) has received significant attention in recent years. HPP tries to find the specific bacteria that can be infected by certain phages, which is fundamental for the applications of targeted phage therapies and interventions. However, the existing HPP methods are mainly based on traditional wet-lab experiments which are laborious and time-consuming. Although certain computational methods have emerged to solve those issues, they perform poorly in genomes and contigs of phages as they neglect the similarity between phages in sequences and protein clusters. In this article, we propose a simple but accurate multi-view attention graph convolutional network (called PGCN) for solving the HPP problem. PGCN first constructs two phage similarity networks as a multi-view graph, which captures the similarity between phages in sequences and protein clusters. Then, PGCN uses a graph convolutional network to capture features of phages from the multi-view graph. Finally, PGCN proposes an adaptive attention mechanism to obtain the integrated features of phages from the multi-view features. Experimental results show the superiority of PGCN over the state-of-the-art methods in host prediction. The results also show the excellent performance of PGCN on host prediction in the metagenomes.