Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.
Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epito...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 432830 - 432831 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2011
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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=104531702&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104531702 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2011 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104531702 2011457113 NLM21876642 PMC3163029 104531702 ppf: 432830 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification. aug: au: Wang, Hsin-Wei Lin, Ya-Chi Pai, Tun-Wen Chang, Hao-Teng affil: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. sug: subj: Bioinformatics Methods Resource Databases Antigens Analysis Algorithms Amino Acids Antigens Models, Theoretical Models, Statistical User-Computer Interface ab: Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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