Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions - Zhai, Yixiao; Chen, Yu; Teng, Zhixia; Zhao, Yuming (Frontiers in Cell and Developmental Biology, 2020)
Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body’s oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method does not use structural information but instead studies antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model's sensitivity for antioxidant protein recognition. We develop a method based on CKSAAP and CT features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set uses 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity is 0.792, the specificity is 0.808, and the average accuracy is 0.8.