Zhang Fan, Liu Chaoyang, Wang Binjie, Chen Xiaopan, Zhang Xinhong
Radiology Department, Huaihe Hospital of Henan University, Kaifeng, 475004, China.
School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.
Interdiscip Sci. 2025 Jun 2. doi: 10.1007/s12539-025-00716-4.
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.
非编码RNA（ncRNAs）是调控基因表达的表观遗传机制的组成部分之一。研究ncRNA-蛋白质相互作用（NPI）有助于探索广泛的生物学特征和相关疾病。传统的NPI研究方法通常需要昂贵的设备、大量的时间和人力。随着传统实验积累的丰富样本，通过计算方法在NPI研究方面取得了显著进展。异构图神经网络是一种深度学习方法，可综合异质类型的数据以及网络拓扑结构。在本研究中，我们提出了一种基于异构图神经网络的用于NPI预测的NPI-HetGNN模型。首先，通过整合ncRNA和蛋白质数据的序列特性以及异质连接的拓扑结构来构建初始特征。然后，获得多级同质子图，并通过元路径游走聚合其语义信息。同时，在子图元路径内融合同质节点信息。为了增强网络的特征提取能力，引入了能量约束自注意力模块。由于缺乏湿实验室验证条件，本研究采用计算验证。通过实验验证了NPI-HetGNN模型在四个基准数据集上的性能。消融实验也证实了我们模型设计的全面性和有效性。实验结果表明，与六种最先进的方法相比，我们的NPI-HetGNN在所有四个数据集上都取得了非常令人满意的结果。
