CBSG-PPI:A Prediction Algorithm for Protein-protein Interaction Based on Graph Neural Networks
To enhance the accuracy of predicting protein-protein interaction(PPI)and to further explore the biological mechanisms be-hind cellular signaling and disease onset,this paper introduced an prediction algorithm abbreviated as CBSG-PPI.Firstly the algorithm processed the k-mer features of proteins using a three-layer feedforward network,and employed the CT method and the Bert method to extract amino acid sequences of proteins,and utilized a convolutional neural network to extract sequence features of proteins.Then it combined a graph neural network and a multilayer perceptron to accurately predict PPI.Compared to existing prediction techniques,CBSG-PPI had shown a clear advantage on public datasets across several key performance metrics such as accuracy,Fl score,recall,and precision,achieving high scores of 0.855,0.853,0.840,and 0.866,respectively.Moreover,the algorithm adopted an improved method for parameter tuning,significantly enhancing computational efficiency,with prediction speeds approximately 140 times faster than traditional algorithms.This substantial improvement in performance not only proves the research value of CBSG-PPI in predicting PPI but also provides a powerful computational tool for the future construction and analysis of protein interaction networks.
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