ACT-SVM: Prediction of Protein-Protein Interactions Based on Support Vector Basis Model

Abstract EN

The interactions between proteins play important roles in several organisms, and such issue can be involved in almost all activities in the cell.

The research of protein-protein interactions (PPIs) can make a huge contribution to the prevention and treatment of diseases.

Currently, many prediction methods based on machine learning have been proposed to predict PPIs.

In this article, we propose a novel method ACT-SVM that can effectively predict PPIs.

The ACT-SVM model maps protein sequences to digital features, performs feature extraction twice on the protein sequence to obtain vector A and descriptor CT, and combines them into a vector.

Then, the feature vectors of the protein pair are merged as the input of the support vector machine (SVM) classifier.

We utilize nonredundant H.

pylori and human dataset to verify the prediction performance of our method.

Finally, the proposed method has a prediction accuracy of 0.727897 for H.

pylori data and a prediction accuracy of 0.838799 for human dataset.

The results demonstrate that this method can be called a stable and reliable prediction model of PPIs.