Predict of Protein Structural Classes based on Gray-Level Co-occurrence Matriz feature of Protein CAI
The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence order effects is an important and challenging problem. Based on the concept of protein cellular automata image, a novel approach for predicting the protein structural classes was introduced. The advantage by incorporating the gray-level co-occurrence matrix(GLCM) feature of cellular automata image into the pseudo amino acid composition as its components is that many important features, which are originally hidden in a long and complicated amino acid sequence, can be clearly revealed thru its cellular automata images. It was demonstrated thru the jackknife cross-validation test that the overall success rate by the new approach was significantly higher than those by the others.
Cellular automata image Protein structural class Pseudo amino-acid composition
Xuan Xiao Pu Wang
School of Mechanical & Electronic Engineering Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333000, C School of Mechanical & Electronic Engineering, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333000,
国际会议
上海
英文
220-223
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）