Chang Liming, Jin Xiu, Rao Yuan, Zhang Xiaodan
College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China.
Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei, 230036, China.
Plant Methods. 2024 Feb 24;20(1):33. doi: 10.1186/s13007-024-01158-7.
More 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.
In 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.
The robustness and effectiveness of our proposed model position it as a valuable approach for advancing the field of miRNA-abiotic stress association prediction.
越来越多的研究表明，miRNA在植物对不同非生物胁迫的响应中起着至关重要的作用。然而，传统的实验方法往往昂贵且效率低下，因此开发高效且经济的计算方法很重要。尽管研究人员已经开发了基于机器学习的方法，但miRNA和非生物胁迫的信息尚未得到充分利用。因此，我们提出了一种基于图神经网络的新方法来预测潜在的miRNA-非生物胁迫关联。
在本研究中，我们充分考虑了来自miRNA和非生物胁迫的多源特征信息，并使用多种相似性度量从不同特征角度计算并整合了miRNA和非生物胁迫的相似性网络。然后，通过异构网络有效地融合上述多源相似性网络以及miRNA与非生物胁迫之间的关联信息。随后，采用重启随机游走（RWR）算法从异构网络中提取全局结构信息，为miRNA和非生物胁迫提供特征向量。之后，我们利用基于图同构网络（GIN）的图自动编码器来学习和重构miRNA-非生物胁迫关联矩阵，以获得潜在的miRNA-非生物胁迫关联。实验结果表明，我们的模型在预测潜在的miRNA-非生物胁迫关联方面优于所有已知方法，在五折交叉验证下，我们模型的AUPR和AUC指标分别达到了98.24%和97.43%。
我们提出的模型的稳健性和有效性使其成为推进miRNA-非生物胁迫关联预测领域的一种有价值的方法。
