Protein–protein interaction (PPI) is very important for various biological processes and has given rise to a series of prediction-computing methods. In spite of different computing methods in relation to PPI prediction, PPI network projects fail to perform on a large scale. Aiming at ensuring that PPI can be predicted effectively, we used a deep neural network (DNN) for the study of PPI prediction that is based on an amino acid sequence. We present a novel DNN-PPI model with an auto covariance (AC) descriptor and a conjoint triad (CT) descriptor for the prediction of PPI that is based only on the protein sequence information. The 10-fold cross-validation indicated that the best DNN-PPI model with CT achieved 97.65% accuracy, 98.96% recall and a 98.51% area under the curve (AUC). The model exhibits a prediction accuracy of 94.20–97.10% for other external datasets. All of these suggest the high validity of the proposed algorithm in relation to various species.

中文翻译：

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DNN-PPI：基于深度神经网络的蛋白质-蛋白质相互作用的大规模预测

蛋白质-蛋白质相互作用（PPI）对于各种生物过程非常重要，并催生了一系列预测计算方法。尽管与 PPI 预测相关的计算方法不同，但 PPI 网络项目无法大规模执行。为了确保 PPI 可以有效预测，我们使用深度神经网络 (DNN) 来研究基于氨基酸序列的 PPI 预测。我们提出了一种新颖的 DNN-PPI 模型，该模型具有自协方差 (AC) 描述符和联合三元组 (CT) 描述符，用于仅基于蛋白质序列信息的 PPI 预测。10 折交叉验证表明，具有 CT 的最佳 DNN-PPI 模型实现了 97.65% 的准确率、98.96% 的召回率和 98.51% 的曲线下面积 (AUC)。该模型的预测精度为 94.20-97。10% 用于其他外部数据集。所有这些都表明了所提出的算法对于各种物种的高度有效性。