A comparison of two machine learning methods for protein secondary structure prediction

被引：3

作者：

Wang, LH ^{[1
]}

Liu, J ^{[1
]}

Zhou, HB ^{[1
]}

机构：

[1] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China

来源：

PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年

关键词：

protein secondary structure prediction; neural network; support vector machine; CB513;

D O I：

10.1109/ICMLC.2004.1378319

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays, the best methods for protein secondary structure prediction are based on neural network and support vector machine, and both of them incorporate the information from multiple sequences alignment. gut the two methods were executed on different training and testing data sets. A comparison between the two methods has been carried on here. We use the most stringent cross validation test procedure to assess the two methods on CB513, which is one of the most popular used data sets. Neural network achieved a Q(3) accuracy of 74.2%, while support vector machine got Q(3) of 76.6%, which was slightly better than NN.

引用

页码：2730 / 2735

页数：6

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