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<p>Accurate identification of ncRNA-protein interactions (NPIs) is critical for understanding various cellular activities and biological functions of ncRNAs and proteins. Many sequence- and/or structure- and graph-based computational approaches have been developed to identify NPIs from large-scale ncRNA and protein data in a high-throughput manner. However, many sequence- and/or structure- and graph-based computational approaches often ignore either the topological information in NPIs or the influence of other molecule networks on NPI prediction. In this work, we propose NPI-HGNN, an end-to-end graph neural network (GNN)-based approach for the identification of NPIs from a large heterogeneous network, consisting of the ncRNA-protein interaction network, the ncRNA-ncRNA similarity network, and the protein–protein interaction network. To our knowledge, NPI-HGNN is the first GNN-based predictor that integrates related heterogeneous networks for NPI prediction. Experiments on five benchmarking datasets demonstrate that NPI-HGNN outperformed several state-of-the-art sequence- and/or structure- and graph-based predictors. In addition, we showcased the prediction power of NPI-HGNN by identifying 12 interacting ncRNAs of the pre-mRNA 3′ end processing protein, which indicates the effectiveness of the proposed model. The source code of NPI-HGNN is freely available for academic purposes at <a href="https://github.com/zhangxin11111/NPI-HGNN">https://github.com/zhangxin11111/NPI-HGNN</a>.</p> Graphical Abstract <p>The workflow of NPI-HGNN involves the following steps. Within the large ncRNA-protein heterogeneous networks (NPHNs), we first extracted enclosing subgraphs for known NPIs and the selected non-NPIs, and encoded the nodes in each enclosing subgraph using feature encoding schemes. Subsequently, we used the graph representation learning module (GRLM) to build an embedding that represents the graph structure. Finally, the outputs of GRLM were used to build the classifier to predict NPIs.</p> <p></p>

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NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions

  • 期刊论文

  • 发表日期：

    2025年2月21日

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  • Xin Zhang,
  • Haofeng Ma,
  • Sizhe Wang,
  • Hao Wu,
  • Yu Jiang,
  • Quanzhong Liu

摘要

Accurate identification of ncRNA-protein interactions (NPIs) is critical for understanding various cellular activities and biological functions of ncRNAs and proteins. Many sequence- and/or structure- and graph-based computational approaches have been developed to identify NPIs from large-scale ncRNA and protein data in a high-throughput manner. However, many sequence- and/or structure- and graph-based computational approaches often ignore either the topological information in NPIs or the influence of other molecule networks on NPI prediction. In this work, we propose NPI-HGNN, an end-to-end graph neural network (GNN)-based approach for the identification of NPIs from a large heterogeneous network, consisting of the ncRNA-protein interaction network, the ncRNA-ncRNA similarity network, and the protein–protein interaction network. To our knowledge, NPI-HGNN is the first GNN-based predictor that integrates related heterogeneous networks for NPI prediction. Experiments on five benchmarking datasets demonstrate that NPI-HGNN outperformed several state-of-the-art sequence- and/or structure- and graph-based predictors. In addition, we showcased the prediction power of NPI-HGNN by identifying 12 interacting ncRNAs of the pre-mRNA 3′ end processing protein, which indicates the effectiveness of the proposed model. The source code of NPI-HGNN is freely available for academic purposes at https://github.com/zhangxin11111/NPI-HGNN.

Graphical Abstract

The workflow of NPI-HGNN involves the following steps. Within the large ncRNA-protein heterogeneous networks (NPHNs), we first extracted enclosing subgraphs for known NPIs and the selected non-NPIs, and encoded the nodes in each enclosing subgraph using feature encoding schemes. Subsequently, we used the graph representation learning module (GRLM) to build an embedding that represents the graph structure. Finally, the outputs of GRLM were used to build the classifier to predict NPIs.