Protein Fold Recognition With Support Vector Machines Fusion Network
- VernacularTitle:基于支持向量机融合网络的蛋白质折叠子识别研究
- Author:
Jianyu SHI
;
Quan PAN
;
Shaowu ZHANG
;
Yan LIANG
- Publication Type:Journal Article
- Keywords:
protein fold recognition, support vector machines (SVM), classifier fusion, dynamic selection
- From:
Progress in Biochemistry and Biophysics
2006;0(02):-
- CountryChina
- Language:Chinese
-
Abstract:
One of the important approaches to structure analysis is protein fold recognition, which is oftenapplied when there is no significant sequence similarity between structurally similar proteins. A framework with athree-layer support vector machines fusion network (SFN) is presented. The framework is applied to 27-classprotein fold recognition from primary structure of proteins. SFN uses support vector machines as memberclassifiers, and adopts All-Versus-All as multi-class categorization. Six groups of features are divided into majorand minor ones by SFN, and several diversity fusion schemes are correspondingly built. The final decision is madeby dynamic selection of the results of all fusion schemes. When it is still difficult to know what kind of fusion offeature groups can achieve good prediction,SFN is a dependable solution by selecting the optimal fusion offeature groups automatically, which can ensure the best recognition. Overall recognition system achieves 61.04%fold prediction accuracy on the independent test dataset. The results and the comparison with other approachesdemonstrate the effectiveness of SFN, and thus encourage its further exploration.
