A large-scale prediction of protein-protein interactions based on random forest and matrix of sequence

Abstract

Protein-protein interaction (PPIs) is an important part of many life activities in organisms, and the prediction of protein-protein interactions is closely related to protein function, disease occurrence, and disease treatment. In order to optimize the prediction performance of protein interactions, here a RT-MOS model was constructed based on Random Forest (RF) and Matrix of Sequence (MOS) to predict protein-protein interactions. Firstly, MOS is used to encode the protein sequences into a 29-dimensional feature vector; Then, a prediction model RT-MOS is build based on random forest, and the RT-MOS model is optimized and evaluated using the test set; Finally, the optimized model RT-MOS is used for prediction. The experimental results show that the accuracy rates of the RT-MOS model on the benchmark dataset and the non-redundant dataset are 97.18% and 91.34%, respectively, and the accuracies on four external datasets of C.elegans, Drosophila, E.coli and H.sapiens are 96.21%, 97.86%, 97.54% and 97.75%, respectively. Compared with the existing methods, it is found that it is superior to the existing methods. The experimental results show that the model RT-MOS has the advantages of saving time, preventing overfitting and high accuracy, and is suitable for large-scale PPIs prediction.