Abstract
Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and dysregulations of these proteins contribute to the development of the complex disease. Free radicals are unstable molecules that seek for electrons from the surrounding atoms for stability. Once a free radical binds to an atom in the body, a chain reaction occurs, which causes damage to cells and DNA. An antioxidant protein is a substance that protects cells from free radical damage. Accurate identification of antioxidant proteins is important for understanding their role in delaying aging and preventing and treating related diseases. Therefore, computational methods to identify antioxidant proteins have become an effective prior-pinpointing approach to experimental verification. In this study, support vector machines was used to identify antioxidant proteins, using amino acid compositions and 9-gap dipeptide compositions as feature extraction, and feature reduction by Principal Component Analysis. The prediction accuracy Acc of this experiment reached 98.38%, the recall rate Sn of the positive sample was found to be 99.27%, the recall rate Sp of the negative sample reached 97.54%, and the M...Continue Reading
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