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 .
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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 {🗺️}

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         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 .
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         dateModified:2016-03-30T00:00:00Z
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            Bioinformatics
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      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 .
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      dateModified:2016-03-30T00:00:00Z
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         Predictive Performance
         Peptide Length
         Length Profile
         Binding Prediction
         9mer Data
         Human Genetics
         Metabolomics
         Bioinformatics
         Medicine/Public Health
         general
         Cancer Research
         Systems Biology
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