Abstract
Protein-protein interactions (PPIs) in plants are crucial for understanding biological processes. Although high-throughput techniques produced valuable information to identify PPIs in plants, they are usually expensive, inefficient, and extremely time-consuming. Hence, there is an urgent need to develop novel computational methods to predict PPIs in plants. In this article, we proposed a novel approach to predict PPIs in plants only using the information of protein sequences. Specifically, plants’ protein sequences are first converted as position-specific scoring matrix (PSSM); then, the fast Walsh–Hadamard transform (FWHT) algorithm is used to extract feature vectors from PSSM to obtain evolutionary information of plant proteins. Lastly, the rotation forest (RF) classifier is trained for prediction and produced a series of evaluation results. In this work, we named this approach FWHT-RF because FWHT and RF are used for feature extraction and classification, respectively. When applying FWHT-RF on three plants’ PPI datasets Maize, Rice, and Arabidopsis thaliana (Arabidopsis), the average accuracies of FWHT-RF using 5-fold cross validation were achieved as high as 95.20%, 94.42%, and 83.85%, respectively. To further evaluate the predictive power of FWHT-RF, we compared it with the state-of-art support vector machine (SVM) and K-nearest neighbor (KNN) classifier in different aspects. The experimental results demonstrated that FWHT-RF can be a useful supplementary method to predict potential PPIs in plants.
Highlights
Protein-protein interactions (PPIs) in plants underlie many biological processes, including cellular organization, signal transduction [1], metabolic cycles [2], and plant defense [3].us, detecting and characterizing the protein interactions are critically important for understanding the relevant molecular mechanisms inside the plant cells
To further evaluate the predictive performance of fast Walsh–Hadamard transform (FWHT)-rotation forest (RF), we compared FWHT-RF with the state-of-art support vector machine (SVM) and k-nearest neighbor (KNN) classifier. e experimental results indicated that FWHT-RF can be a complement tool to large-scale prediction of PPIs in plants
We employed multiple evaluation indicators to access the effectiveness of FWHT-RF, including accuracy (Acc.), sensitivity (Sen.), precision (Prec.), Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC)
Summary
Protein-protein interactions (PPIs) in plants underlie many biological processes, including cellular organization, signal transduction [1], metabolic cycles [2], and plant defense [3]. DPPI combined random projection and data augmentation with a deep, Siamese-like convolutional neural network to predict PPIs. Zhang et al [15] presented the EnsDNN (Ensemble Deep Neural) method, which first employed the local descriptor, covariance descriptor, and multiscale continuous and discontinuous local descriptor together to explore the interactions between proteins. Kulmanov et al [18] developed a method called DeepGO, which combined a deep ontologyaware classifier with amino acid sequence information to detect protein functions and interactions. Despite these advances, there is still room for improvement in the prediction performance of PPIs’ model [19]. To further evaluate the predictive performance of FWHT-RF, we compared FWHT-RF with the state-of-art support vector machine (SVM) and k-nearest neighbor (KNN) classifier. e experimental results indicated that FWHT-RF can be a complement tool to large-scale prediction of PPIs in plants
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