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  5. How Does Bmcbioinformatics.biomedcentral.com Make Money
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We are analyzing https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-446.

Title:
POPISK: T-cell reactivity prediction using support vector machines and string kernels | BMC Bioinformatics | Full Text
Description:
Background Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide
Website Age:
25 years and 11 months (reg. 1999-08-06).

Matching Content Categories {📚}

  • Education
  • Science
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Content Management System {📝}

What CMS is bmcbioinformatics.biomedcentral.com built with?

Custom-built

No common CMS systems were detected on Bmcbioinformatics.biomedcentral.com, and no known web development framework was identified.

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What is the average monthly size of bmcbioinformatics.biomedcentral.com audience?

🚀 Good Traffic: 50k - 100k visitors per month


Based on our best estimate, this website will receive around 53,076 visitors per month in the current month.

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Display Ads {🎯}


The website utilizes display ads within its content to generate revenue. Check the next section for further revenue estimates.

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Display Ads {🎯}

$730 per month
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Keywords {🔍}

peptides, pubmed, prediction, immunogenicity, google, scholar, article, peptide, binding, mhc, cas, popisk, positions, properties, tcell, physicochemical, immunogenic, method, figure, class, important, central, bioinformatics, data, reactivity, amino, string, dataset, hlaa, authors, recognition, structural, position, imma, performance, sequences, immune, tcr, sequence, structures, svm, bmc, antigen, studies, acids, predicting, methods, affinity, kernel, complex,

Topics {✒️}

acc =  tp + tn tp + tn + fp + fn a6-tcr/peptide/hla-a2 structures open access article shinn-ying ho t-cell clone jm22 t-cell reactivity published virus-specific ctl responses major histocompatibility complex human t-lymphotropic virus hla-dq8beta cell peptidome exploiting physico-chemical properties identifying t-cell receptor solvent-accessible surface areas object-oriented molecular visualization original cancer/testis antigen subsequent t-cell reactivity predicting t-cell reactivity modified cancer/testis antigen predict t-cell reactivity t-cell reactivity prediction individual peptide side-chains t-cell reactivity related mhc molecule hla-a2 allele-specific motifs revealed single-residue modifications reported tw/popisk/download t-cell epitope prediction support vector machine quantitative structure-activity relationship t-cell recognition positions nested 10-fold cross-validation hla-a2-binding peptides hla-a2 binding peptides large-scale statistical studies privacy choices/manage cookies negative t-cell selection mhc-peptide-tcr complex tcr-peptide-mhc complex mhc-peptide-tcr interaction tcr-peptide-mhc interaction tcr-peptide-mhc interactions tcr-peptide-mhc caused 10-fold cross-validation accuracy authors scientific editing genetic algorithm optimization article tung virus-epitope vaccine design weighted string kernel support vector machines mhc-ii binding affinity

Schema {🗺️}

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         description:Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK .
         datePublished:2011-11-15T00:00:00Z
         dateModified:2011-11-15T00:00:00Z
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      headline:POPISK: T-cell reactivity prediction using support vector machines and string kernels
      description:Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK .
      datePublished:2011-11-15T00:00:00Z
      dateModified:2011-11-15T00:00:00Z
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         Major Histocompatibility Complex
         Major Histocompatibility Complex Class
         Transporter Associate With Antigen Processing
         Area Under Receiver Operate Characteristic Curve
         String Kernel
         Bioinformatics
         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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                     type:PostalAddress
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            name:Shinn-Ying Ho
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               name:Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
               type:PostalAddress
            type:Organization
      name:Andreas Kämper
      affiliation:
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               type:PostalAddress
            type:Organization
      name:Oliver Kohlbacher
      affiliation:
            name:Eberhard Karls University Tübingen
            address:
               name:Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Shinn-Ying Ho
      affiliation:
            name:National Chiao Tung University
            address:
               name:Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
               type:PostalAddress
            type:Organization
            name:National Chiao Tung University
            address:
               name:Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:School of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan
      name:Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
      name:Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
      name:Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
      name:Center for Bioinformatics Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
      name:Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
      name:Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan

Social Networks {👍}(2)

External Links {🔗}(260)

Analytics and Tracking {📊}

  • Google Tag Manager

Libraries {📚}

  • Backbone.js
  • Prism.js

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