Predicting NcRNA-protein Interactions Based On The Relational Graph Convolutional Network Auto-Encoder

Non-coding RNA(nc RNA)used to be considered as "noise" or "dark matters" in the genome.However,as more and more different types of nc RNAs were discovered,molecular functions of nc RNAs have also been further studied.nc RNA plays a key role in a variety of biological processes.nc RNA achieves its molecular functions by interacting with other biomacromolecules.Among them,the most important interaction object is protein.Therefore,the discovery of new nc RNA-protein interactions is of great significance to the study of nc RNA functions.Identifying nc RNA-protein interactions by experimental methods is costly and time-consuming,so more and more computational methods are proposed as alternative methods.This paper proposed a new method for predicting nc RNA-protein interactions:NPI-RGCNAE(predicting nc RNA-protein interactions using the Relational Graph Convolutional Network Auto-Encoder).Firstly,this method calculated the interaction score for each nc RNA-protein pair based on protein sequence similarities.Reliable negative samples were screened according to the score.Secondly,on the basis of constructing the bipartite graphs corresponding to the positive and negative samples respectively,the Relational Graph Convolutional Network(R-GCN)was used as the encoder.The node embeddings were learned from the bipartite graphs of the positive and negative relations at the same time.Finally,NPI-RGCNAE used Dist Mult as the decoder.Each pair of node embeddings were input into the decoder to reconstruct the relationship between them.NPI-RGCNAE can simultaneously aggregate the topological information from the network of positive samples and negative samples for interaction prediction in a short time.Therefore,NPI-RGCNAE only needs less than 10% of the training time of other methods to obtain performance similar to other existing methods.NPI-RGCNAE also introduced a more reliable negative sample screening method.This method can not only improve the performance of NPI-RGCNAE,but also improve the prediction accuracy of other existing methods.The experimental results show that NPI-RGCNAE is an efficient,accurate and robust method for predicting nc RNA-protein interactions.All datasets and source codes involved in the research of this paper have been archived in a public Github repository(https://github.com/Angelia0hh/NPI-RGCNAE).