Coronavirus HIV Influnenza hepatitis B hepatitis C EBV HPV HTLV-1 Virus Database
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Epitope prediction Mutation prediction
viral disease outbreak forecasting Evolution Dynamics
Transmission Dynamics Infectious disease intelligence

T cell epitope prediction

1 T Cell Epitope Predictions (review ) 2020
2 Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides(2023)
3 IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity(2023)
4 A robust deep learning workflow to predict CD8+T-cell epitopes . 2023
5 Prediction and validation of murine MHC class I epitopes of the recombinant virus VSV-GP(2023)
6 HLA-I and HLA-II Peptidomes of SARS-CoV-2: A Review(2023)
7 NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data (2020,Highly Cited Paper)
8 MSBooster: improving peptide identification rates using deep learning-based features(2023)
9 Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity(2023) NATURE MACHINE INTELLIGENCE
10 IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity(2023)
11 IUP-BERT: Identification of Umami Peptides Based on BERT Features . 2022
12 MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction (2023)
13 DeepNeo: a webserver for predicting immunogenic neoantigens(2023)
14 A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction(2023)
15 TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning(2023)
16 Can we predict T cell specificity with digital biology and machine learning?(2023)
17 STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides(2023)
18 Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity(2023)NATURE MACHINE INTELLIGENCE
19 Binding peptide generation for MHC Class I proteins with deep reinforcement learning(2023)
20 A transformer-based model to predict peptide-HLA class I binding and optimize mutated peptides for vaccine design.(2022)NATURE MACHINE INTELLIGENCE
21 Ranking Peptide Binders by Affinity with AlphaFold(2023)
22 Epitope-Evaluator: An interactive web application to study predicted T-cell epitopes(2022)
23 HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction(2022)
24 Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review(2022)
25 SARS-CoV-2 human T cell epitopes: Adaptive immune response against COVID-19(2021)
26 Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases(2021)
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B cell epitope prediction

1 NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes(2023)
2 Critical review of conformational B-cell epitope prediction methods(2023)
3 BepiPred-3.0: Improved B-cell epitope prediction using protein language models(2022)
4 EpiDope: a deep neural network for linear B-cell epitope prediction(2021)
5 epitope1D: accurate taxonomy-aware B-cell linear epitope prediction(2023)
6 An in silico approach for prediction of B cell and T cell epitope candidates against Chikungunya virus(2023)
7 DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes(2023)
8 LBCE-XGB: A XGBoost Model for Predicting Linear B-Cell Epitopes Based on BERT Embeddings(2023)
9 LBCEPred: a machine learning model to predict linear B-cell epitopes(2022)
10 Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model(2023)
11 EpitopeVec: linear epitope prediction using deep protein sequence embeddings(2021)
12 SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning(2022)
13 BepiTBR: T-B reciprocity enhances B cell epitope prediction(2022)
14 epitope3D: a machine learning method for conformational B-cell epitope prediction(2022)
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T-cell receptor–antigen binding recognition

1 Pan-Peptide Meta Learning for T-cell receptor-antigen binding recognition . 2023
2 TEINet: a deep learning framework for prediction of TCR-epitope binding specificity . 2023
3 VDJdb: a curated database of T-cell receptor sequences with known antigen specificity . 2018
4 McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences . 2017
5 Can we predict T cell specificity with digital biology and machine learning? 2023
6 TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning . 2023
7 Deep learning-based prediction of the T cell receptor-antigen binding specificity . NATURE MACHINE INTELLIGENC 2021
8 epiTCR: a highly sensitive predictor for TCR-peptide binding(2023)
9 Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning . NATURE MACHINE INTELLIGENCE 2023
10 Quantitative approaches for decoding the specificity of the human T cell repertoire(2023)
11 SARS-CoV-2 epitope-specific T cells: Immunity response feature, TCR repertoire characteristics and cross-reactivity (2023)
12 MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations(2023)
13 TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning(2023)
14 Structure-based prediction of T cell receptor:peptide-MHC interactions(2023)
15 MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations .2023
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Influenza virus

Influenza is divided into three types: A, B, and C. The influenza virus discovered in recent years will be classi