Comprehensive Review and Comparison of Anticancer Peptides Identification Models

(E-pub Ahead of Print)

Author(s): Xiao Song, Yuanying Zhuang*, Yihua Lan*, Yinglai Lin, Xiaoping Min

Journal Name: Current Protein & Peptide Science

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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, we provide a comprehensive review of existing machine learning methods for ACPs prediction and fair comparison of the predictors. To evaluate current prediction tools, we conducted a comparative study and analyzed the existing ACPs predictor from 10 public literatures. The comparative results obtained suggest that 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.

Keywords: anticancer peptides; machine learning; feature representation

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(E-pub Ahead of Print)
DOI: 10.2174/1389203721666200117162958
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