Protein Secondary Structure Prediction Based on Improved SVM Method in Compound Pyramid Model
Methods for predicting protein secondary structure provide information that is useful both in ab initio structure prediction and as additional restraints for fold recognition algorithms. Secondary structure predictions may also be used to guide the design of site directed mutagenesis studies, and to locate potential functionally important residues. In this article, we propose a method of improved SVM for predicting protein secondary structure. Using evolutionary information contained in amino acids physicochemical properties, position-specific scoring matrix generated by psi-blast as input to improved SVM, secondary structure can be predicted at significantly increased accuracy. Based on KDTICM theory, we have constructed a compound pyramid model, which is composed of four layers of the intelligent interface and integrated in several ways, such as improved SVM, mixed-modal BP, KDD* method and so on. On the RS126 data set, state overall per-residue accuracy, Q3 reached 83.06％, while SOV99 accuracy increased to 80.6％.On the CB513 data set, Q3 reached 80.49％, SOV99 accuracy increased to 79.84％.This article briefly introduces this model and highlights the improved SVM method.
Protein Second Structure SVM Compound Pyramid Model
Bingru Yang Wu Qu Yun Zhai Haifeng Sui
School of Information Engineering,University of Science and Technology Beijing,Beijing 100083 School of Information Engineering,University of Science and Technology Beijing,Beijing 100083 Colleg
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
4405-4410
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）