Xiao Song , Yuanying Zhuang *, Yihua Lan *, Yinglai Lin and Xiaoping Min Pages 201 - 210 ( 10 )
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.
Anticancer peptides, machine learning, feature representation, SVM, AAC, binary profiles, ACPs.
School of Informatics, Xiamen University, Xiamen 361005, School of Informatics, Xiamen University, Xiamen 361005, School of Computer and Information Technology, Nanyang Normal University, Nanyang 473000, School of Informatics, Xiamen University, Xiamen 361005, School of Informatics, Xiamen University, Xiamen 361005