Improving protein secondary structure prediction: the evolutionary optimized classification algorithms

Determining protein structures plays an important role in the field of drug design. Currently, the machine learning methods including artificial neural network (ANN) and support vector machine (SVM) have replaced the experimental techniques to determine these structures. However, as these predictions are increasingly becoming the workhorse for numerous methods aimed at predicting protein structure and function, it still needs to be improved. In this study, evolutionary optimized neural network (EONN) and evolutionary optimized support vector machine (EOSVM) were applied to predict protein secondary structure using GA, DE, and PSO. Despite the simplicity of the applied methods, the results are found to be superior to those achieved through other techniques. The EONN and EOSVM modestly improved the accuracy by 6% and 5% on the same database, respectively.