【摘要】 为提高蛋白质二级结构预测精度,提出一种新的网络模型和编码方法。首先利用基因表达式编程(GEP)的全局搜索能力同时进化设计神经网络的结构和连接权;其次,对神经网络输入层编码进行了改进,添加了氨基酸残基所处的疏水环境。用PDB-Select25中的36条蛋白质共6122个残基进行测试,结果表明提出的网络模型和编码方法能有效提高蛋白质二级结构预测的精度。更多还原

【Abstract】 In order to improve the prediction accuracy of protein secondary structure,a new network model and its coding method are proposed.Firstly,the structure and connection weights of BP network are evolved simultaneously by using global research ability of GEP.Secondly,the coding method of neural network is improved by integrating the hydrophobic value around the residue.The model is employed to predict 36 nonhomologous protein sequences with 6,122 residues in PDBSelect25,the results show that the proposed model and coding method can efficiently improve the prediction accuracy.更多还原