Classification of Protein Homo-oligomers Using Support Vector Machine

Shao Wu Zhang, Quan Pan, Run Sheng Chen, Hong Cai Zhang

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

The homo-dimer, homo-trimer, homo-tetramer and homo-hexamer of protein were classified using both of support vector machine and Bayes covariant discriminant methods. It was found that the total accuracies of "one-versus-rest" and "all-versus-all" are 77.36% and 93. 43% respectively using support vector machine in jackknife test, which are 26. 72 and 42. 79 percentile higher respectively than that of Bayes covariant discriminant method in the same test. These results show that the support vector machine is a specially effective method for classifying the higher protein homo-oligomers from protein primary sequences. Using "all-versus-all" policy is better than "one-versus-rest" policy for classifying homo-oligomers based on the same machine learning method (such as support vector machine). And it was also indicated that the primary sequences of homo-oligomeric proteins contain quaternary information.

源语言英语
页(从-至)879-883
页数5
期刊Progress in Biochemistry and Biophysics
30
6
出版状态已出版 - 12月 2003

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