ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy and < em > N < /em > -Peptide Combination

In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.PMID:33927782 | PMC:PMC8049822 | DOI:10.1155/2021/5518209
Source: Computational and Mathematical Methods in Medicine - Category: Statistics Authors: Source Type: research