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• W4285807029 abstract "Antioxidant proteins play a vital role in diseases prevention caused by free radical intermediates. Accurate identification of antioxidant proteins may provide significant clues to improve their fundamental understanding in the field of bioinformatics and pharmacology. Prediction of antioxidant protein is meaningful and a challenging task. In this study, we develop a novel predictor called PredAoDP based on two different descriptors, which incorporates salient evolutionary profile features with support vector machine (SVM), to predict antioxidant proteins. The evolutionary information in the PSI-BLAST profile is encoded and transformed into a series of fixed-length feature vectors by extracting 20-D, amino acid composition and 400-D bigram features from position-specific scoring matrix (PSSM). As a result, a feature vector of 420-dimensional (420-D) feature vector via serial combination was constructed from two training datasets of antioxidant protein Z1 and Z2. The descriptors are then fed to the SVM for classification along with jack-knife cross-validation test method for evaluating the prediction performance. Our proposed intelligent predictor achieved ACC, 93.18% and MCC, 0.712 for Z1 similarly for Z2 datasets ACC, 97.89% and MCC, 0.949 by jack-knife cross-validation test. To evaluate the generalization abilities of the developed method, we performed an independent test and achieved superior performance compared the other published approaches. The experimental results revealed the effectiveness of the proposed approach and can be utilized as a reliable tool for predicting large-scale antioxidant proteins in particular and other proteins in general. The source code and benchmark datasets are available at https://github.com/saeed344/PreAoDp." @default.
• W4285807029 created "2022-07-19" @default.
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• W4285807029 date "2022-09-01" @default.
• W4285807029 modified "2023-10-15" @default.
• W4285807029 title "PredAoDP: Accurate identification of antioxidant proteins by fusing different descriptors based on evolutionary information with support vector machine" @default.
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• W4285807029 doi "https://doi.org/10.1016/j.chemolab.2022.104623" @default.
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