We are analyzing https://link.springer.com/article/10.1186/s13073-016-0288-x.

Title:
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets | Genome Medicine
Description:
Background Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Results Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. Conclusions We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Science
Education
Technology & Computing

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

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

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month

Based on our best estimate, this website will receive around 7,626,432 visitors per month in the current month.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We don’t know how the website earns money.

Many websites are intended to earn money, but some serve to share ideas or build connections. Websites exist for all kinds of purposes. This might be one of them. Link.springer.com might be cashing in, but we can't detect the method they're using.

Keywords {🔍}

mhc, binding, peptides, length, peptide, data, method, article, mer, class, molecules, prediction, affinity, allmer, pubmed, methods, performance, google, scholar, trained, predicted, predictive, lengths, panspecific, information, ligands, rank, alleles, nielsen, multiple, percentile, ligand, cas, allelespecific, threshold, distribution, networks, amino, terms, syfpeithi, compared, binders, approach, dataset, acids, values, predictions, characterized, training, set,

Topics {✒️}

pan-allele/pan-length approach translates mhc-ligand-source protein combinations machine-learning algorithm article download pdf gradient descent back-propagation pan-specific mhc class accurate pan-specific prediction pan-specific prediction model expert end-user discovery hiv-1-infected human cells mhc class ii mhc-ligand source protein pan-length training pipeline human leukocyte antigen-class inserting/deleting amino acids l-mer approximation relies pan-specific training approach pan-specific training procedure allele-specific training pipeline major histocompatibility complex pan-specific method trained allele-length combination characterized binding prediction algorithm peptide-mhc binding predictions sampled peptide-space leads machine learning allele-specific length preference allele-specific method trained privacy choices/manage cookies shorter/longer query peptides improved prediction accuracy generating immunodominant cd8+ pan-specific mhc artificial neural networks predicted peptide-mhc binders approach significantly improves hla class i pan-specific approach improved predictive performance receptor-ligand system characterized simple approximation approach creative commons license pan-specific method binding core location mhc allelic variants selecting peptides based peptide-mhc class quantitative data including monkey mhc class allmer method consistently

Questions {❓}

How many MHC ligands are captured at different rank scores?

Schema {🗺️}

WebPage:
      mainEntity:
         headline:NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets
         description:Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .
         datePublished:2016-03-30T00:00:00Z
         dateModified:2016-03-30T00:00:00Z
         pageStart:1
         pageEnd:9
         license:http://creativecommons.org/publicdomain/zero/1.0/
         sameAs:https://doi.org/10.1186/s13073-016-0288-x
         keywords:
            Predictive Performance
            Peptide Length
            Length Profile
            Binding Prediction
            9mer Data
            Human Genetics
            Metabolomics
            Bioinformatics
            Medicine/Public Health
            general
            Cancer Research
            Systems Biology
         image:
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig1_HTML.gif
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig2_HTML.gif
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig3_HTML.gif
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig4_HTML.gif
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig5_HTML.gif
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig6_HTML.gif
         isPartOf:
            name:Genome Medicine
            issn:
               1756-994X
            volumeNumber:8
            type:
               Periodical
               PublicationVolume
         publisher:
            name:BioMed Central
            logo:
               url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
               type:ImageObject
            type:Organization
         author:
               name:Morten Nielsen
               affiliation:
                     name:Universidad Nacional de San Martín
                     address:
                        name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
                        type:PostalAddress
                     type:Organization
                     name:Technical University of Denmark
                     address:
                        name:Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
                        type:PostalAddress
                     type:Organization
               email:[email protected]
               type:Person
               name:Massimo Andreatta
               affiliation:
                     name:Universidad Nacional de San Martín
                     address:
                        name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
                        type:PostalAddress
                     type:Organization
               type:Person
         isAccessibleForFree:1
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets
      description:Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .
      datePublished:2016-03-30T00:00:00Z
      dateModified:2016-03-30T00:00:00Z
      pageStart:1
      pageEnd:9
      license:http://creativecommons.org/publicdomain/zero/1.0/
      sameAs:https://doi.org/10.1186/s13073-016-0288-x
      keywords:
         Predictive Performance
         Peptide Length
         Length Profile
         Binding Prediction
         9mer Data
         Human Genetics
         Metabolomics
         Bioinformatics
         Medicine/Public Health
         general
         Cancer Research
         Systems Biology
      image:
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig1_HTML.gif
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig2_HTML.gif
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig3_HTML.gif
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig4_HTML.gif
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig5_HTML.gif
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13073-016-0288-x/MediaObjects/13073_2016_288_Fig6_HTML.gif
      isPartOf:
         name:Genome Medicine
         issn:
            1756-994X
         volumeNumber:8
         type:
            Periodical
            PublicationVolume
      publisher:
         name:BioMed Central
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Morten Nielsen
            affiliation:
                  name:Universidad Nacional de San Martín
                  address:
                     name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
                     type:PostalAddress
                  type:Organization
                  name:Technical University of Denmark
                  address:
                     name:Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
            name:Massimo Andreatta
            affiliation:
                  name:Universidad Nacional de San Martín
                  address:
                     name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
                     type:PostalAddress
                  type:Organization
            type:Person
      isAccessibleForFree:1
["Periodical","PublicationVolume"]:
      name:Genome Medicine
      issn:
         1756-994X
      volumeNumber:8
Organization:
      name:BioMed Central
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:Universidad Nacional de San Martín
      address:
         name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
         type:PostalAddress
      name:Technical University of Denmark
      address:
         name:Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
         type:PostalAddress
      name:Universidad Nacional de San Martín
      address:
         name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Morten Nielsen
      affiliation:
            name:Universidad Nacional de San Martín
            address:
               name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
               type:PostalAddress
            type:Organization
            name:Technical University of Denmark
            address:
               name:Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Massimo Andreatta
      affiliation:
            name:Universidad Nacional de San Martín
            address:
               name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
      name:Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
      name:Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina

External Links {🔗}(120)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

4.09s.

LINK . SPRINGER . COM {}