Wei Meng-Meng, Yu Chang-Qing, Li Li-Ping, You Zhu-Hong, Ren Zhong-Hao, Guan Yong-Jian, Wang Xin-Fei, Li Yue-Chao
School of Information Engineering, Xijing University, Xi'an, China.
College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, China.
Front Genet. 2023 Feb 10;14:1122909. doi: 10.3389/fgene.2023.1122909. eCollection 2023.
LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.
长链非编码RNA（lncRNA）与蛋白质的相互作用在许多人类疾病的发生发展及治疗中发挥着重要作用。鉴于确定lncRNA与蛋白质相互作用的实验方法既昂贵又耗时，且目前可用的计算方法较少，因此，开发高效且准确的lncRNA与蛋白质相互作用预测方法迫在眉睫。在这项研究中，我们提出了一种基于元路径的异质网络嵌入模型，即LPIH2V。该异质网络由lncRNA相似性网络、蛋白质相似性网络和已知的lncRNA-蛋白质相互作用网络组成。利用网络嵌入的HIN2Vec方法在异质网络中提取行为特征。结果表明，在五折交叉验证测试中，LPIH2V的曲线下面积（AUC）为0.97，准确率（ACC）为0.95。该模型成功地展现出优越性和良好的泛化能力。与其他模型相比，LPIH2V不仅通过相似性提取属性特征，还通过在异质网络中的元路径游走获取行为属性。LPIH2V将有助于预测lncRNA与蛋白质之间的相互作用。
