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.
Keywords: Anticancer peptides, machine learning, feature representation, SVM, AAC, binary profiles, ACPs.
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