外文摘要：BackgroundMore and more studies show that miRNA plays a crucial role in plants' response to different abiotic stresses. However, traditional experimental methods are often expensive and inefficient, so it is important to develop efficient and economical computational methods. Although researchers have developed machine learning-based method, the information of miRNAs and abiotic stresses has not been fully exploited. Therefore, we propose a novel approach based on graph neural networks for predicting potential miRNA-abiotic stress associations.ResultsIn this study, we fully considered the multi-source feature information from miRNAs and abiotic stresses, and calculated and integrated the similarity network of miRNA and abiotic stress from different feature perspectives using multiple similarity measures. Then, the above multi-source similarity network and association information between miRNAs and abiotic stresses are effectively fused through heterogeneous networks. Subsequently, the Restart Random Walk (RWR) algorithm is employed to extract global structural information from heterogeneous networks, providing feature vectors for miRNA and abiotic stress. After that, we utilized the graph autoencoder based on GIN (Graph Isomorphism Networks) to learn and reconstruct a miRNA-abiotic stress association matrix to obtain potential miRNA-abiotic stress associations. The experimental results show that our model is superior to all known methods in predicting potential miRNA-abiotic stress associations, and the AUPR and AUC metrics of our model achieve 98.24% and 97.43%, respectively, under five-fold cross-validation.ConclusionsThe robustness and effectiveness of our proposed model position it as a valuable approach for advancing the field of miRNA-abiotic stress association prediction. We propose an innovative approach based on multi-source similarity network fusion and graph autoencoder for predicting potential miRNA-abiotic stress associations.We pioneer the application of graph neural networks in predicting miRNA-abiotic stress associations, achieving more accurate predictive performance than all known methods.We also introduce a machine learning model based on multi-source similarity network fusion, showcasing its superiority over existing machine learning-based models.
外文关键词：Graph neural network;miRNA-abiotic stress association;Multi-source features;Graph autoencoder
作者：Rao, Yuan;Jin, Xiu;Chang, Liming;Zhang, Xiaodan
作者单位：Anhui Agr Univ
期刊名称：PLANT METHODS
期刊影响因子：0.0
出版年份：2024
出版刊次：20(1)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台