PSPGO: Cross-Species Heterogeneous Network Propagation for Protein Function Prediction
Kaitao Wu, Lexiang Wang, Bo Liu, Yang Liu, Yadong Wang, Junyi Li
Abstract
How to use computational methods to effectively predict the function of proteins remains a challenge. Most prediction methods based on single species or single data source have some limitations: the former need to train different models for different species, the latter only to infer protein function from a single perspective, such as the method only using Protein-Protein Interaction (PPI) network just considers the protein environment but ignore the intrinsic characteristics of protein sequences. We found that in some network-based multi-species methods the networks of each species are isolated, which means there is no communication between networks of different species. To solve these problems, we propose a cross-species heterogeneous network propagation method based on graph attention mechanism, PSPGO, which can propagate feature and label information on sequence similarity (SS) network and PPI network for predicting gene ontology terms. Our model is evaluated on a large multi-species dataset split based on time and is compared with several state-of-the-art methods. The results show that our method has good performance. We also explore the predictive performance of PSPGO for a single species. The results illustrate that PSPGO also performs well in prediction for single species.