Application of improved three-dimensional kernel approach to prediction of protein structural class.
Kernel methods, such as kernel PCA, kernel PLS, and support vector machines, are widely known machine learning techniques in biology, medicine, chemistry, and material science. Based on nonlinear mapping and Coulomb function, two 3D kernel approaches were improved and applied to predictions of the f...
| Publicado en: | BioMed Research International Vol. 2013; pp. 625403 - 625404 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104083234&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104083234 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104083234 2012194675 NLM23878814 PMC3708390 104083234 ppf: 625403 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Application of improved three-dimensional kernel approach to prediction of protein structural class. aug: au: Liu, Xu Zhang, Yuchao Yang, Hua Wang, Lisheng Liu, Shuaibing affil: School of Chemistry & Chemical Engineering, Guangxi University, Guangxi Province, Nanning 530004, China. sug: subj: Models, Theoretical Proteins Sequence Analysis Methods Amino Acids Computer Simulation Documentation Molecular Structure Proteins Classification ab: Kernel methods, such as kernel PCA, kernel PLS, and support vector machines, are widely known machine learning techniques in biology, medicine, chemistry, and material science. Based on nonlinear mapping and Coulomb function, two 3D kernel approaches were improved and applied to predictions of the four protein tertiary structural classes of domains (all-α, all-β, α/β, and α+β) and five membrane protein types with satisfactory results. In a benchmark test, the performances of improved 3D kernel approach were compared with those of neural networks, support vector machines, and ensemble algorithm. Demonstration through leave-one-out cross-validation on working datasets constructed by investigators indicated that new kernel approaches outperformed other predictors. It has not escaped our notice that 3D kernel approaches may hold a high potential for improving the quality in predicting the other protein features as well. Or at the very least, it will play a complementary role to many of the existing algorithms in this regard. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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