Bioinformatics | Oncology DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity. June 22, 2024
Immunology | Oncology The identification of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex. June 12, 2024
Immunology | Oncology Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy. February 21, 2024
Bioinformatics | Immunology DeepMHCI: An anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction. September 5, 2023
Bioinformatics | Immunology DeepTAP: An RNN-based method of TAP-binding peptide prediction in the selection of tumor neoantigens. July 16, 2023
Immunology | Oncology iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features. May 25, 2023
Bioinformatics | Immunology | Oncology DeepNeo: a webserver for predicting immunogenic neoantigens. April 18, 2023
Bioinformatics | Immunology | Oncology Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction. October 19, 2022
Immunology | Oncology Deep learning-based prediction of the T cell receptor-antigen binding specificity. August 25, 2022
Bioinformatics | Immunology | Oncology dbPepNeo2.0: A Database for Human Tumor Neoantigen Peptides From Mass Spectrometry and TCR Recognition. May 2, 2022
Immunology | Oncology IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity. June 24, 2020
Bioinformatics | Immunology | Oncology Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. January 8, 2019