Protein–protein interactions (PPIs) in plants play an essential role in the regulation of biological processes. However, traditional experimental methods are expensive, time-consuming, and need sophisticated technical equipment. These drawbacks motivated the development of novel computational approaches to predict PPIs in plants. In this article, a new deep learning framework, which combined the discrete Hilbert transform (DHT) with deep neural networks (DNN), was presented to predict PPIs in plants. To be more specific, plant protein sequences were first transformed as a position-specific scoring matrix (PSSM). Then, DHT was employed to capture features from the PSSM. To improve the prediction accuracy, we used the singular value decomposition algorithm to decrease noise and reduce the dimensions of the feature descriptors. Finally, these feature vectors were fed into DNN for training and predicting. When performing our method on three plant PPI datasets Arabidopsis thaliana, maize, and rice, we achieved good predictive performance with average area under receiver operating characteristic curve values of 0.8369, 0.9466, and 0.9440, respectively. To fully verify the predictive ability of our method, we compared it with different feature descriptors and machine learning classifiers. Moreover, to further demonstrate the generality of our approach, we also test it on the yeast and human PPI dataset. Experimental results anticipated that our method is an efficient and promising computational model for predicting potential plant–protein interacted pairs.

植物中的蛋白质-蛋白质相互作用（PPI）在生物过程的调节中发挥着重要作用。然而，传统的实验方法成本昂贵、耗时且需要精密的技术设备。这些缺点促使人们开发出新的计算方法来预测植物中的 PPI。在本文中，提出了一种新的深度学习框架，该框架将离散希尔伯特变换 (DHT) 与深度神经网络 (DNN) 相结合，用于预测植物中的 PPI。更具体地说，植物蛋白质序列首先被转化为位置特异性评分矩阵（PSSM）。然后，使用 DHT 来捕获 PSSM 的特征。为了提高预测精度，我们使用奇异值分解算法来降低噪声并降低特征描述符的维度。最后，这些特征向量被输入到 DNN 中进行训练和预测。在三个植物 PPI 数据集上执行我们的方法时拟南芥、玉米和水稻，我们取得了良好的预测性能，接收者操作特征曲线下的平均面积值分别为 0.8369、0.9466 和 0.9440。为了充分验证我们方法的预测能力，我们将其与不同的特征描述符和机器学习分类器进行了比较。此外，为了进一步证明我们方法的通用性，我们还在酵母和人类 PPI 数据集上进行了测试。实验结果表明我们的方法是一种有效且有前途的计算模型，用于预测潜在的植物-蛋白质相互作用对。