NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets
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Title
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets
Authors
Keywords
Predictive Performance, Peptide Length, Length Profile, Binding Prediction, 9mer Data
Journal
Genome Medicine
Volume 8, Issue 1, Pages -
Publisher
Springer Nature
Online
2016-03-30
DOI
10.1186/s13073-016-0288-x
References
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Note: Only part of the references are listed.- Automated benchmarking of peptide-MHC class I binding predictions
- (2015) Thomas Trolle et al. BIOINFORMATICS
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- (2014) Yohan Kim et al. BMC BIOINFORMATICS
- A Comparative Study of HLA Binding Affinity and Ligand Diversity: Implications for Generating Immunodominant CD8+ T Cell Responses
- (2014) X. Rao et al. JOURNAL OF IMMUNOLOGY
- The immune epitope database (IEDB) 3.0
- (2014) Randi Vita et al. NUCLEIC ACIDS RESEARCH
- The IPD and IMGT/HLA database: allele variant databases
- (2014) James Robinson et al. NUCLEIC ACIDS RESEARCH
- Identification and characterisation of peptide binding motifs of six autoimmune disease-associated human leukocyte antigen-class I molecules includingHLA-B*39:06
- (2014) M. Eichmann et al. TISSUE ANTIGENS
- NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ
- (2013) Edita Karosiene et al. IMMUNOGENETICS
- Tapasin Facilitation of Natural HLA-A and -B Allomorphs Is Strongly Influenced by Peptide Length, Depends on Stability, and Separates Closely Related Allomorphs
- (2013) L. Geironson et al. JOURNAL OF IMMUNOLOGY
- HLA Peptide Length Preferences Control CD8+ T Cell Responses
- (2013) M. J. Rist et al. JOURNAL OF IMMUNOLOGY
- HLA Class I Alleles Are Associated with Peptide-Binding Repertoires of Different Size, Affinity, and Immunogenicity
- (2013) S. Paul et al. JOURNAL OF IMMUNOLOGY
- NetMHCcons: a consensus method for the major histocompatibility complex class I predictions
- (2011) Edita Karosiene et al. IMMUNOGENETICS
- NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data
- (2011) Massimo Andreatta et al. PLoS One
- HLArestrictor—a tool for patient-specific predictions of HLA restriction elements and optimal epitopes within peptides
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- Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior
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- NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
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- Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers
- (2008) C. Lundegaard et al. BIOINFORMATICS
- NetMHCpan, a method for MHC class I binding prediction beyond humans
- (2008) Ilka Hoof et al. IMMUNOGENETICS
- NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11
- (2008) Claus Lundegaard et al. NUCLEIC ACIDS RESEARCH
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