Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection StrategyReportar como inadecuado

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Antioxidant proteins perform significant functions in maintaining oxidation-antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation-antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI Secondary Structure Information, PSSM Position Specific Scoring Matrix, RSA Relative Solvent Accessibility, and CTD Composition, Transition, Distribution. The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF Random Forest, SMO Sequential Minimal Optimization, NNA Nearest Neighbor Algorithm, and J48 with an accuracy of 0.925. A Relief combined with IFS Incremental Feature Selection method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC Matthew’s Correlation Coefficient of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at

Autor: Lina Zhang, Chengjin Zhang , Rui Gao, Runtao Yang, Qing Song



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