Prediction of protein structural classes using support vector machines

Amino Acids
X-D Sun, R-B Huang

Abstract

The support vector machine, a machine-learning method, is used to predict the four structural classes, i.e. mainly alpha, mainly beta, alpha-beta and fss, from the topology-level of CATH protein structure database. For the binary classification, any two structural classes which do not share any secondary structure such as alpha and beta elements could be classified with as high as 90% accuracy. The accuracy, however, will decrease to less than 70% if the structural classes to be classified contain structure elements in common. Our study also shows that the dimensions of feature space 20(2) = 400 (for dipeptide) and 20(3) = 8 000 (for tripeptide) give nearly the same prediction accuracy. Among these 4 structural classes, multi-class classification gives an overall accuracy of about 52%, indicating that the multi-class classification technique in support of vector machines may still need to be further improved in future investigation.

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Citations

Oct 13, 2006·Amino Acids·H-B Shen, K-C Chou
Jan 5, 2008·Amino Acids·Loris Nanni, Alessandra Lumini
Apr 23, 2008·Amino Acids·Xing-Ming ZhaoKazuyuki Aihara
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Apr 24, 2008·Amino Acids·Yonge Feng, Liaofu Luo
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Jan 21, 2016·Computational and Mathematical Methods in Medicine·Yunyun LiangShengli Zhang
Apr 22, 2008·Journal of Theoretical Biology·Chao ChenPei-Xiang Cai
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