Comprehensive Review and Comparison of Anticancer Peptides Identification Models

Anticancer peptides (ACPs) eliminate pathogenic bacteria and kill tumor cells, showing no hemolysis and no damages to normal human cells. This unique ability explores the possibility of ACPs as therapeutic delivery and its potential applications in clinical therapy. Identifying ACPs is one of the most fundamental and central problems in new antitumor drug research. During the past decades, a number of machine learning-based prediction tools have been developed to solve this important task. However, the predictions produced by various tools are difficult to quantify and compare. Therefore, in this article, a comprehensive review of existing machine learning methods for ACPs prediction and fair comparison of the predictors is provided. To evaluate current prediction tools, a comparative study was conducted and analyzed the existing ACPs predictor from the 10 public works of literature. The comparative results obtained suggest that the Support Vector Machine-based model with features combination provided significant improvement in the overall performance when compared to the other machine learning method-based prediction models.

Automated summary

This study provides a comprehensive evaluation of machine learning methods for ACPs prediction and a fair comparison of existing predictors. The Support Vector Machine-based model with features combination showed significant improvement in overall performance compared to other machine learning methods.