ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

• Published in: Briefings in bioinformatics Vol. 21; no. 5; pp. 1846 - 1855
• Main Authors: Rao, Bing, Zhou, Chen, Zhang, Guoying, Su, Ran, Wei, Leyi
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.09.2020; Oxford Publishing Limited (England)
• Subjects: Anticancer properties; Antitumor activity; Cancer; Learning algorithms; Machine learning; Peptides; Prediction models; Proteins; Representations; Servers; anticancer peptide; random forest; feature representation; machine learning

Abstract Fast and accurate identification of the peptides with anticancer activity potential from large-scale proteins is currently a challenging task. In this study, we propose a new machine learning predictor, namely, ACPred-Fuse, that can automatically and accurately predict protein sequences wit...