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Title: Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learning
Abstract

Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA–proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, diseases, etc. Traditional experimental methods can accomplish this work but are often labor-intensive and expensive. Machine learning and deep learning methods have achieved great success by exploiting sufficient sequence or structure information. Graph Neural Network (GNN)-based methods consider the topology in ncRNA–protein graphs and perform well on tasks like NPI prediction. Based on GNN, some pairwise constraint methods have been developed to apply on homogeneous networks, but not used for NPI prediction on heterogeneous networks. In this paper, we construct a pairwise constrained NPI predictor based on dual Graph Convolutional Network (GCN) called NPI-DGCN. To our knowledge, our method is the first to train a heterogeneous graph-based model using a pairwise learning strategy. Instead of binary classification, we use a rank layer to calculate the score of an ncRNA–protein pair. Moreover, our model is the first to predict NPIs on the ncRNA–protein bipartite graph rather than the homogeneous graph. We transform the original ncRNA–protein bipartite graph into two homogenous graphs on which to explore more » second-order implicit relationships. At the same time, we model direct interactions between two homogenous graphs to explore explicit relationships. Experimental results on the four standard datasets indicate that our method achieves competitive performance with other state-of-the-art methods. And the model is available at https://github.com/zhuoninnin1992/NPIPredict

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Authors:
; ; ; ;
Publication Date:
NSF-PAR ID:
10372079
Journal Name:
Briefings in Bioinformatics
Volume:
23
Issue:
6
ISSN:
1467-5463
Publisher:
Oxford University Press
Sponsoring Org:
National Science Foundation
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