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

DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction

22 Apr 2021

Abstract excerpt

Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed and demonstrated with their excellent performances for peptide-HLA class I binding prediction. However, there is still a lack of effective binding prediction models...

Read the complete abstract on PubMed

Topics

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

Share to Antimicrobial Peptides and Activities
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: 33886473
DOI: 10.1109/tcbb.2021.3074927

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

DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding PredictionPMID 33886473Read ↗

DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding predictionSemantic proximity Read ↗

DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding predictionSemantic proximity Read ↗

Attention mechanism-based deep learning pan-specific model for interpretable MHC-I peptide binding predictionSemantic proximity Read ↗

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity predictionSemantic proximity Read ↗

Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.Semantic proximity Read ↗

CcBHLA: pan-specific peptide–HLA class I binding prediction via Convolutional and BiLSTM featuresSemantic proximity Read ↗

OnionMHC: A Deep Learning Model for Peptide - HLA-A*02:01 Binding Predictions using both Structure and Sequence Feature SetsSemantic proximity Read ↗

HLA class I binding prediction via convolutional neural networksSemantic proximity Read ↗

MHCAttnNet: predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model.Semantic proximity Read ↗

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

Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.Semantic proximity Read ↗

Connecting MHC-I-binding motifs with HLA alleles via deep learning.Semantic proximity Read ↗

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