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Literature DB >> 11868916

Prediction of protein structural classes by support vector machines.

Yu-Dong Cai¹, Xiao-Jun Liu, Xue-biao Xu, Kuo-Chen Chou.

Abstract

In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino and composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.

Mesh：

Substances：
Proteins

Year: 2002 PMID： 11868916 DOI： 10.1016/s0097-8485(01)00113-9

Source DB: PubMed Journal: Comput Chem ISSN： 0097-8485

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Cited

31 in total

1. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence.

Authors: C Z Cai; L Y Han; Z L Ji; X Chen; Y Z Chen
Journal: Nucleic Acids Res Date: 2003-07-01 Impact factor: 16.971

2. Prediction of RNA-binding proteins from primary sequence by a support vector machine approach.

Authors: Lian Yi Han; Cong Zhong Cai; Siew Lin Lo; Maxey C M Chung; Yu Zong Chen
Journal: RNA Date: 2004-03 Impact factor: 4.942

3. QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.

Authors: H X Liu; R S Zhang; X J Yao; M C Liu; Z D Hu; B T Fan
Journal: J Comput Aided Mol Des Date: 2004-06 Impact factor: 3.686

4. Prediction of milk/plasma drug concentration (M/P) ratio using support vector machine (SVM) method.

Authors: Chunyan Zhao; Haixia Zhang; Xiaoyun Zhang; Ruisheng Zhang; Feng Luan; Mancang Liu; Zhide Hu; Botao Fan
Journal: Pharm Res Date: 2006-11-30 Impact factor: 4.200

5. An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition.

Authors: Jiang Wu; Meng-Long Li; Le-Zheng Yu; Chao Wang
Journal: Protein J Date: 2010-01 Impact factor: 2.371

Prediction of protein structural classes by support vector machines.

1. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence.

2. Prediction of RNA-binding proteins from primary sequence by a support vector machine approach.

3. QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.

4. Prediction of milk/plasma drug concentration (M/P) ratio using support vector machine (SVM) method.

5. An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition.

6. Diagnosis of several diseases by using combined kernels with Support Vector Machine.

7. Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information.

8. Support vector machine-based feature selection for classification of liver fibrosis grade in chronic hepatitis C.

9. A novel fusion based on the evolutionary features for protein fold recognition using support vector machines.

10. Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.