Kong Meng, Zhang Yusen, Xu Da, Chen Wei, Dehmer Matthias
School of Mathematics and Statistics, Shandong University at Weihai, Weihai, China.
University of Applied Sciences Upper Austria, School of Management, Steyr, Austria.
Front Genet. 2020 Feb 4;11:18. doi: 10.3389/fgene.2020.00018. eCollection 2020.
The task of predicting protein-protein interactions (PPIs) has been essential in the context of understanding biological processes. This paper proposes a novel computational model namely FCTP-WSRC to predict PPIs effectively. Initially, combinations of the F-vector, composition (C) and transition (T) are used to map each protein sequence onto numeric feature vectors. Afterwards, an effective feature extraction method PCA (principal component analysis) is employed to reconstruct the most discriminative feature subspaces, which is subsequently used as input in weighted sparse representation based classification (WSRC) for prediction. The FCTP-WSRC model achieves accuracies of 96.67%, 99.82%, and 98.09% for , and datasets respectively. Furthermore, the FCTP-WSRC model performs well when predicting three significant PPIs networks: the single-core network (CD9), the multiple-core network (Ras-Raf-Mek-Erk-Elk-Srf pathway), and the cross-connection network (Wnt-related Network). Consequently, the promising results show that the proposed method can be a powerful tool for PPIs prediction with excellent performance and less time.
在理解生物过程的背景下，预测蛋白质-蛋白质相互作用（PPI）的任务至关重要。本文提出了一种新颖的计算模型，即FCTP-WSRC，以有效地预测PPI。首先，使用F向量、组成（C）和转移（T）的组合将每个蛋白质序列映射到数值特征向量上。之后，采用一种有效的特征提取方法——主成分分析（PCA）来重建最具判别力的特征子空间，随后将其用作基于加权稀疏表示分类（WSRC）的预测输入。FCTP-WSRC模型在 、 和 数据集上分别达到了96.67%、99.82%和98.09%的准确率。此外，FCTP-WSRC模型在预测三个重要的PPI网络时表现良好：单核网络（CD9）、多核网络（Ras-Raf-Mek-Erk-Elk-Srf途径）和交叉连接网络（Wnt相关网络）。因此，这些有前景的结果表明，所提出的方法可以成为一个用于PPI预测的强大工具，具有出色的性能且耗时较少。
