Abstract
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 H. pylori, Human and Yeast 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.
Highlights
Investigating protein–protein interactions (PPIs) relate to examine the correlation between proteins involved in various aspects of life processes such as signal transduction, gene expression regulation, energy metabolism, and cell cycle regulation
We employ five fold cross validation to evaluate the performance of the FCTP-weighted sparse representation based classification (WSRC) model
Given the fact that computational tools for predicting PPIs have been used over years, only a few of them are able to predict quickly, and accurately
Summary
Investigating protein–protein interactions (PPIs) relate to examine the correlation between proteins involved in various aspects of life processes such as signal transduction, gene expression regulation, energy metabolism, and cell cycle regulation. The traditional way of studying individual proteins has failed to meet the requirements of the post-genome era because the performance of proteins is diverse and dynamic when performing physiological functions. Proteins should be studied at the global, network, and dynamic levels. By studying the sum of all proteins can we support the understanding of life's behavioral processes, disease prevention, and development of new drugs (Long et al, 2019). Some researchers predict PPIs by biological methods such as yeast two-hybrid screening (Ito et al, 2001; Pazos and Valencia, 2002) and affinity purification (Gavin et al, 2002). The results obtained by wet-lab experiments usually contain a large amount of false positive and false negative data, and these methods are time
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