Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Prediction of epitopes using neural network based methods.

Journal of immunological methods - 30 Nov 2011

Lundegaard Claus, Lund Ole, Nielsen Morten

Abstract excerpt

In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We have updated the prediction servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, have been evaluated to be among the very best performing MHC:peptide binding predictors available. Here we describe the...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Epitope Mapping
Share to Epitopes, T-Lymphocyte

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Histocompatibility Antigens Class I

Share to Internet

Share to Models, Molecular

Share to Neural Networks, Computer

Share to Peptides

Source

Open in PubMed Open full text

Identifiers

PMID: 21047511
DOI: 10.1016/j.jim.2010.10.011
PMCID: PMC3134633

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026. Manifest publication-graph-cohort-2026-09-05.1.

Source receipt: pubmed, retrieved 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of epitopes using neural network based methods.PMID 21047511Read ↗

NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.Semantic proximity Read ↗

Predicting MHC class I epitopes in large datasets.Semantic proximity Read ↗

Sensitive quantitative predictions of peptide‐MHC binding by a ‘Query by Committee’ artificial neural network approachSemantic proximity Read ↗

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.Semantic proximity Read ↗

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopesSemantic proximity Read ↗

AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictionsSemantic proximity Read ↗

NetMHCcons: a consensus method for the major histocompatibility complex class I predictions.Semantic proximity Read ↗

epitopepredict: A tool for integrated MHC binding predictionSemantic proximity Read ↗

Evaluation of machine learning methods to predict peptide binding to MHC Class I proteinsSemantic proximity Read ↗

Methods for Prediction of Peptide Binding to MHC Molecules: A Comparative StudySemantic proximity Read ↗

A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes.Semantic proximity Read ↗

TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.