Prediction of Protein–Protein Interactions in Arabidopsis, Maize, and Rice by Combining Deep Neural Network With Discrete Hilbert Transform - Pan, Jie; Li, Li-Ping; You, Zhu-Hong; Yu, Chang-Qing; Ren, Zhong-Hao; Guan, Yong-Jian (Frontiers in Genetics, 2021)
rotein-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, that 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 Position-specific scoring matrix (PSSM). Then, DHT was employed to capture features from PSSM. To improve the prediction accuracy, we used the Singular Value Decomposition (SVD) algorithm to decrease noise and reduce the dimensions of feature descriptors. Finally, these feature vectors were fed into Deep neural networks for training and predicting. When performing our method on three plants PPIs datasets Arabidopsis thaliana (A. thaliana), Maize (Zea mays) and Rice (Oryza sativa), we achieved good predictive performance with average AUC values of 0.8369, 0.9466 and 0.9440, respectively. To fully verify the predictive ability of our method, we compared it with some 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 PPIs dataset. Experimental results anticipated that our method is an efficient and promising computational model for predicting potential plant-protein interacted pairs.