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

DeepNeo: a webserver for predicting immunogenic neoantigens

18 Apr 2023

Abstract excerpt

Non-self epitopes, whether originated from foreign substances or somatic mutations, trigger immune responses when presented by major histocompatibility complex (MHC) molecules and recognized by T cells. Identification of immunogenically active neoepitopes has significant implications in cancer and virus medicine. However, current methods are mostly limited to predicting physical binding of mutant peptides and...

Read the complete abstract on PubMed

Topics

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

Share to Immunotherapy and Immune Responses
Share to Monoclonal and Polyclonal Antibodies Research
Share to vaccines and immunoinformatics approaches

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 37070174
DOI: 10.1093/nar/gkad275

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

DeepNeo: a webserver for predicting immunogenic neoantigensPMID 37070174Read ↗

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide–human leukocyte antigen complexesSemantic proximity Read ↗

DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.Semantic proximity Read ↗

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggetsSemantic proximity Read ↗

IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.Semantic proximity Read ↗

TSNAdb: A Database for Tumor-Specific Neoantigens from Immunogenomics Data AnalysisSemantic proximity Read ↗

NEPdb: A Database of T-Cell Experimentally-Validated Neoantigens and Pan-Cancer Predicted Neoepitopes for Cancer ImmunotherapySemantic proximity Read ↗

Predicting T cell recognition of MHC class I restricted neoepitopesSemantic proximity Read ↗

ScanNeo2: a comprehensive workflow for neoantigen detection and immunogenicity prediction from diverse genomic and transcriptomic alterationsSemantic proximity Read ↗

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity PredictionSemantic proximity Read ↗

Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapySemantic proximity Read ↗

Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy.Semantic proximity Read ↗

Population-level distribution and putative immunogenicity of cancer neoepitopesSemantic proximity Read ↗

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