NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.

Abstract

Non-coding RNAs (ncRNAs) are one of the components of epigenetic mechanisms that regulates gene expression. Studying ncRNA-protein interactions (NPI) can help to explore a wide range of biological features and related diseases. Traditional NPI research methods often require expensive equipment, a lot of time and labor. With the abundant samples accumulated from traditional experiments, remarkable progress has been made in the study of NPI by computational methods. Heterogeneous graph neural network is a deep learning method that synthesizes heterogeneous types of data as well as network topology. In this study, we propose an NPI-HetGNN model for NPI prediction based on heterogeneous graph neural networks. Firstly, initial features are constructed by integrating the sequence properties of ncRNA and protein data as well as the topology of heterogeneous connections. Then, the multilevel homogeneous subgraph is obtained and its semantic information is aggregated by metapath walking. At the same time, the homogeneous node information is fused within the subgraph metapath. To enhance feature extraction ability of the network, an energy-constrained self-attention module is introduced. Due to the lack of wet lab validation conditions, this study adopts computational verification. The performance of the NPI-HetGNN model on four benchmark datasets is experimentally verified. Ablation experiments also confirmed the comprehensiveness and validity of our model design. The experimental results show that comparing with six state-of-the-art methods, our NPI-HetGNN achieves very satisfactory results on all four datasets.