Title:Survey of In-silico Prediction of Anticancer Peptides
Volume: 21
Issue: 15
Author(s): Nan Ye*
Affiliation:
- School of Finance and Economics, Xinyang Agriculture and Forestry University, Xinyang 464000,China
Keywords:
Anticancer peptide, In-silico, Machine learning, Feature construction, Feature selection, Cancer.
Abstract: Cancer is one of the major causes of death in human beings. While traditional cancer
treatments kill cancerous cells, they negatively affect normal cells. In addition, the side effects and
high medical costs of treatment prevent effective management of cancer. Nonetheless, anticancer
peptides have gained popularity over the recent years as potential therapeutic agents that may complement
traditional therapies. Compared to conventional wet-lab experiments, computation-based
methods provide a promising platform for high-throughput identification of peptides that have anticancer
activity. Therefore, this review summarizes the currently available databases for anticancer
peptides/proteins. This is a survey of 22 recently published in-silico methods that aim to predict anticancer
peptides accurately. More specifically, the article details the benchmark datasets, feature
construction, feature selection, machine learning algorithms, assessment criteria, comparison of different
methods, and publicly available predictors. We also compare the prediction performance of
these predictors to the benchmark dataset. Finally, the study makes several recommendations concerning
the future development of databases for anticancer peptides and methods that can be used
to predict anticancer peptides.
