Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.
It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an oct...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/15/2015
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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=109273941&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273941 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/15/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273941 109273941 109273941 10.1155/2015/263586 109273941 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction. aug: au: Liu, Hui Shi, Xiaomiao Guo, Dongmei Zhao, Zuowei Yimin affil: Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China sug: subj: HIV-1 Protease Inhibitors Peptide Hydrolases Metabolism Drug Design Human Amino Acids Funding Source Sensitivity and Specificity Peptides Anti-HIV Agents ab: It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1′, and p2′ are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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