ANOX: A robust computational model for predicting the antioxidant proteins based on multiple features
As an indispensable component of various living organisms, the antioxidant proteins have been studied for anti-aging and prevention of various diseases, such as altitude sickness, coronary heart disease, and even cancer. However, the traditional experimental methods for identifying the antioxidant p...
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Veröffentlicht in: | Analytical biochemistry 2021-10, Vol.631, p.114257-114257, Article 114257 |
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Sprache: | eng |
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Zusammenfassung: | As an indispensable component of various living organisms, the antioxidant proteins have been studied for anti-aging and prevention of various diseases, such as altitude sickness, coronary heart disease, and even cancer. However, the traditional experimental methods for identifying the antioxidant proteins are very expensive and time-consuming. Thus, to address the challenge, a new predictor, named ANOX, was developed in this study. Multiple features, such as frequency matrix features (FRE), amino acid and dipeptide composition (AADP), evolutionary difference formula features (EEDP), k-separated bigrams (KSB), and PSI-PRED secondary structure (PRED), were extracted to generate the original feature space. To find the optimized feature subset, the Max-Relevance-Max-Distance (MRMD) algorithm was implemented for feature ranking and our model received the best performance with the top 1170 features. Rigorous tests were performed to evaluate the performance of ANOX, and the results showed that ANOX achieved a major improvement in the prediction accuracy of the antioxidant proteins (AUC:0.930 and 0.935 using 5-fold cross-validation or the jackknife test) compared to the state-of-the-art predictor AOPs-SVM (AUC:0.869 and 0.885). The dataset used in this study and the source code of ANOX are all available at https://github.com/NWAFU-LiuLab/ANOX.
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•An SVM-based model was proposed to identify the antioxidant proteins.•Multiple features were extracted to generate the original feature space.•The Max-Relevance-Max-Distance algorithm is implemented for feature selection.•Our model performs better than the state-of-the art predictor. |
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ISSN: | 0003-2697 1096-0309 |
DOI: | 10.1016/j.ab.2021.114257 |