ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.

ABSTRACT: Cancer is the second-leading cause of death worldwide, and therapeutic peptides that target and destroy cancer cells have received a great deal of interest in recent years. Traditional wet experiments are expensive and inefficient for identifying novel anticancer peptides; therefore, the development of an effective computational approach is essential to recognize ACP candidates before experimental methods are used. In this study, we proposed an Ada-boosting algorithm with the base learner random forest called ACP-ADA, which integrates binary profile feature, amino acid index, and amino acid composition with a 210-dimensional feature space vector to represent the peptides. Training samples in the feature space were augmented to increase the sample size and further improve the performance of

SUBMITTER: Bhattarai S 

PROVIDER: S-EPMC9603247 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature