used a descriptor called a \"signature product\" to discover PPIs [12]. The signature product is a product of sub-sequences from a protein sequence and extends the signature descriptor from chemical information. [PERSON] et al. presented a conjoint triad (CT) approach to take the characters of amino acids and their adjacent amino acids into account [13]. [PERSON] et al. proposed an auto covariance (AC) descriptor approach to represent an amino acid sequence with a foundation of seven different physicochemical scales [14]. When they detected Yeast PPIs, prospective prediction accuracy was achieved. [PERSON] et al. employed the physicochemical property response matrix combined with the local phase quantization descriptor (PR-LPQ) to extract the eigen value of the proteins [15]. Considering the evolutionary information of protein, [PERSON] et al. adopted substitution matrix representation (SMR) based on BLOSUM62 to construct a feature vector and achieved promising prediction accuracy [16]. [PERSON] et al. proposed a novel protein sequence representation method based on a matrix to predict PPIs via an ensemble classification method [17]. [PERSON] et al. proposed a computational method based on a probabilistic classification vector machine (PCVM) model and a Zernike moment (ZM) descriptor to identify PPIs from amino acids sequences [18]. [PERSON] et al. employed the NABCAM (the neighbor affinity-based core-attachment method) to identify protein complexes from dynamic PPI networks [19]. [PERSON] et al. summarized and evaluated a couple of feature extraction methods for describing protein amino acids sequences by verifying them on multiple datasets, and they constructed an ensemble of classifiers for sequence-based protein classification, which not only performed well on many datasets but was also, under certain conditions, superior to the state of the art [20; 21; 22; 23]. Next, the computational methods for PPI prediction can be formulated as a binary classification problem. A number of machine learning-based computational models for PPI prediction have emerged. [PERSON] et al. proposed a technique of applying Bayesian networks to detect PPIs on the Yeast dataset [24]. [PERSON] et al. employed several classifiers, including support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR), to compare their performances in predicting PPIs [25]. Of machine-learning-based computational models for PPI prediction, one of the most important challenges is that the high-dimensional features may include unimportant information and noise, leading to the over-fitting of classification systems [26; 27]. Previous works have shown that random projection (RP) is a high-efficiency and sufficient precision approach that can reduce the dimensions of many high-dimensional datasets [28; 29; 30]. However, the performance using a single RP method is poor [31] because of the instability of RP. Therefore, in our study, an RP ensemble method was designed to predict PPIs. The capacity of the integration model was better and more effective than that of separate runs of the RP approach. Moreover, the ensemble algorithm achieved results that are also superior to those of similar schemes that use principal component analysis (PCA) to reduce the dimensionality of the dataset [32]. The RP-ensemble-based classifier has useful features [33]. Firstly, RP maintains the geometrical structure of the dataset with a certain distortion rate when its dimensionality is reduced. This feature can reduce the complicacy of the classifier and the difficulty of new sample classification. In addition, dimensionality reduction can eliminate redundant information and reduce generalization error. Particularly, instead of relying on a single classifier, the RP ensemble method incorporates several classifiers that are superior to each single classifier. This feature can lead to better and more stable classification results.