Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC

Antioxidant proteins are considered crucial in the areas of research on life sciences and pharmacology. They prevent damage to cells and DNA which are caused by free radicals. The role of antioxidants in the ageing process makes them more significant in their accurate identification. Disease preventions through antioxidant protein have also been the area of study in recent past. The existing process to identify and test every single antioxidant protein in order to obtain its properties is inefficient and expensive. Due to this nature, many pharmaceutical agents have reflected antioxidant proteins as attractive targets. Approaches based on computational methodologies have appeared to be as a highly desirable resource in the annotation and determination process of antioxidant proteins. In this study, we have developed a method that is built on computation intelligence and statistical moments based features for prediction. Our proposed system has achieved better accuracy than state-of-art systems in the prediction of antioxidant proteins from non-antioxidant proteins using 10-fold-cross-validation tests. These outcomes suggest that the use of statistical moments with a multilayer neural network could bear more effective and efficient results.