ACP_MS: prediction of anticancer peptides based on feature extraction. Issue 6 (2nd November 2022)

• Record Type: Journal Article
• Authors: Zhou, Caimao; Peng, Dejun; Liao, Bo; Jia, Ranran; Wu, Fangxiang
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-11-02
• Subjects: anticancer peptide -- monoMonoKGap method -- digital features -- stochastic gradient descent -- ACP_MS model; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac462
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc