Study of protein secondary structure prediction methods based on GEP-BP network ensemble
基于GEP-BP网络集成的蛋白质二级结构预测方法研究*

In order to improve the prediction accuracy of protein secondary structure,this paper presented a new prediction model composed of two-level network based on GEP-BP network ensemble.Firstly,evolved simultaneously the structure and connection weights of BP network were by using global research ability of GEP, then trained fatherly all the individuals of last generation by BP algorithm and formed the first-level through a combination method to ensemble part of individuals. Secondly, according to the dependency of neighboring neural network output,refined the results of the first-level by the second-level network.Employed the model to predict 36 nonhomologous protein sequences with 6122 residues in PDBSelect25.The results show that the proposed model can efficiently improve the prediction accuracy, increasing prediction accuracy to 73.02%.