| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/bioinformatics/TrolleMGKSLSPN15 |
| P356 | DOI | 10.1093/BIOINFORMATICS/BTV123 |
| P932 | PMC publication ID | 4481849 |
| P698 | PubMed publication ID | 25717196 |
| P5875 | ResearchGate publication ID | 272837490 |
| P50 | author | John Sidney | Q88013439 |
| Yohan Kim | Q89811159 | ||
| Alessandro Sette | Q28324161 | ||
| Morten Nielsen | Q37828921 | ||
| Thomas Trolle | Q51083473 | ||
| Jason A Greenbaum | Q57954590 | ||
| Ole Lund | Q59198225 | ||
| Bjoern Peters | Q60541156 | ||
| P2093 | author name string | Imir G Metushi | |
| P2860 | cites work | Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method | Q24815448 |
| The role of naive T cell precursor frequency and recruitment in dictating immune response magnitude | Q26998750 | ||
| ERAAP customizes peptides for MHC class I molecules in the endoplasmic reticulum | Q28205720 | ||
| Inhibitors of the proteasome block the degradation of most cell proteins and the generation of peptides presented on MHC class I molecules | Q28248180 | ||
| NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence | Q28469279 | ||
| Evaluation of template-based models in CASP8 with standard measures | Q29048208 | ||
| EVA: continuous automatic evaluation of protein structure prediction servers. | Q30329275 | ||
| CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL. | Q30353649 | ||
| Assessment of template-free modeling in CASP10 and ROLL. | Q30356925 | ||
| Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior | Q30932156 | ||
| A community resource benchmarking predictions of peptide binding to MHC-I molecules | Q33247448 | ||
| SVRMHC prediction server for MHC-binding peptides | Q33261251 | ||
| Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research | Q33325636 | ||
| Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research | Q33393949 | ||
| The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding | Q33419581 | ||
| The immune epitope database 2.0. | Q33600879 | ||
| Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses | Q33652450 | ||
| Degradation of cell proteins and the generation of MHC class I-presented peptides | Q33652524 | ||
| Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions | Q33953557 | ||
| A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus | Q34536936 | ||
| NetMHCpan, a method for MHC class I binding prediction beyond humans | Q35869130 | ||
| Immune epitope database analysis resource | Q36088427 | ||
| Evidence that transporters associated with antigen processing translocate a major histocompatibility complex class I-binding peptide into the endoplasmic reticulum in an ATP-dependent manner | Q36574363 | ||
| NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11 | Q36749382 | ||
| Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods | Q37090212 | ||
| The role of the proteasome in the generation of MHC class I ligands and immune responses | Q37851052 | ||
| Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools. | Q37939072 | ||
| Peptidases trimming MHC class I ligands | Q38054314 | ||
| Measurement of MHC/peptide interactions by gel filtration | Q39989093 | ||
| Quantitative online prediction of peptide binding to the major histocompatibility complex | Q40548223 | ||
| NetMHCcons: a consensus method for the major histocompatibility complex class I predictions | Q44856955 | ||
| MHC binding prediction with KernelRLSpan and its variations. | Q45957590 | ||
| Machine learning competition in immunology - Prediction of HLA class I binding peptides. | Q45961424 | ||
| Selective identification of HLA-DP4 binding T cell epitopes encoded by the MAGE-A gene family. | Q51130525 | ||
| Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. | Q53677823 | ||
| P433 | issue | 13 | |
| P407 | language of work or name | English | Q1860 |
| P304 | page(s) | 2174-2181 | |
| P577 | publication date | 2015-02-25 | |
| P1433 | published in | Bioinformatics | Q4914910 |
| P1476 | title | Automated benchmarking of peptide-MHC class I binding predictions | |
| P478 | volume | 31 |
| Q53822443 | Amino acid substitutions within HLA-B*27-restricted T cell epitopes prevent recognition by hepatitis delta virus-specific CD8+ T cells. |
| Q47243904 | An automated benchmarking platform for MHC class II binding prediction methods |
| Q28546655 | An open-source computational and data resource to analyze digital maps of immunopeptidomes |
| Q39838760 | Analyzing the effect of peptide-HLA-binding ability on the immunogenicity of potential CD8+ and CD4+ T cell epitopes in a large dataset |
| Q30277773 | Characterization of the peptide binding specificity of the HLA class I alleles B*38:01 and B*39:06. |
| Q89842436 | Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort |
| Q38885361 | Computational genomics tools for dissecting tumour-immune cell interactions |
| Q47158481 | Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction |
| Q61443414 | DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction |
| Q39730450 | Genome sequence of Candida versatilis and comparative analysis with other yeast |
| Q27324007 | HLA class I molecular variation and peptide-binding properties suggest a model of joint divergent asymmetric selection |
| Q102379297 | Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools |
| Q64101502 | Identification of novel human leukocyte antigen-A*11:01-restricted cytotoxic T-lymphocyte epitopes derived from osteosarcoma antigen papillomavirus binding factor |
| Q36304147 | Immunoinformatics and epitope prediction in the age of genomic medicine |
| Q46265999 | Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data |
| Q51359804 | In Silico Prediction of Linear B-Cell Epitopes on Proteins. |
| Q40188864 | In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes |
| Q47290509 | Informatics for cancer immunotherapy |
| Q89965526 | Major Histocompatibility Complex Binding, Eluted Ligands, and Immunogenicity: Benchmark Testing and Predictions |
| Q26781372 | Malaria vaccines: identifying Plasmodium falciparum liver-stage targets |
| Q38947679 | Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction |
| Q41089843 | Mycobacterium tuberculosis Complex Exhibits Lineage-Specific Variations Affecting Protein Ductility and Epitope Recognition |
| Q30008748 | NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions. |
| Q36742441 | NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets |
| Q33785947 | PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity |
| Q58707974 | Pan-cancer analysis of neoepitopes |
| Q92072903 | Peptide-Major Histocompatibility Complex Class I Binding Prediction Based on Deep Learning With Novel Feature |
| Q55383910 | Predicting HLA CD4 Immunogenicity in Human Populations. |
| Q58129532 | Predicting T cell recognition of MHC class I restricted neoepitopes |
| Q89790830 | Predicting clinical benefit of immunotherapy by antigenic or functional mutations affecting tumour immunogenicity |
| Q55518761 | SNPnexus: assessing the functional relevance of genetic variation to facilitate the promise of precision medicine. |
| Q58587953 | Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes |
| Q56535000 | Systems Immunology: Learning the Rules of the Immune System |
| Q38372371 | TANTIGEN: a comprehensive database of tumor T cell antigens |
| Q41827687 | TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates |
| Q36553815 | The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference |
| Q30278636 | The common equine class I molecule Eqca-1*00101 (ELA-A3.1) is characterized by narrow peptide binding and T cell epitope repertoires |
| Q38798968 | The role of neoantigens in response to immune checkpoint blockade. |
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