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Data from: MHCBN: A comprehensive database of MHC binding and non-binding...
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Updated Aug 17, 2025+ more versions
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EmailClick to copy linkLink copiedCite(2025). MHCBN: A comprehensive database of MHC binding and non-binding peptides [Dataset]. http://identifiers.org/RRID:SCR_007785Unique identifierhttps://identifiers.org/RRID:SCR_007785Dataset updatedAug 17, 2025DescriptionThe MHCBN is a curated database consisting of detailed information about Major Histocompatibility Complex (MHC) Binding,Non-binding peptides and T-cell epitopes. The version 4.0 of database provides information about peptides interacting with TAP and MHC linked autoimmune diseases.
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Number of binding/non-binding peptides in MHCBN benchmark dataset.
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xlsUpdated Jun 6, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYasser EL-Manzalawy; Drena Dobbs; Vasant Honavar (2023). Number of binding/non-binding peptides in MHCBN benchmark dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0003268.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0003268.t002Dataset updatedJun 6, 2023Dataset provided byPLOS ONEAuthorsYasser EL-Manzalawy; Drena Dobbs; Vasant HonavarLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionUPDS refers to datasets of non-redundant peptides. The last three columns refer to similarity-reduced datasets (see text for details).
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AUC values for the three methods evaluated on MHCBN-UPDS datasets.
- plos.figshare.com
xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYasser EL-Manzalawy; Drena Dobbs; Vasant Honavar (2023). AUC values for the three methods evaluated on MHCBN-UPDS datasets. [Dataset]. http://doi.org/10.1371/journal.pone.0003268.t011xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0003268.t011Dataset updatedMay 31, 2023Dataset provided byPLOS ONEAuthorsYasser EL-Manzalawy; Drena Dobbs; Vasant HonavarLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionFor each dataset, the rank of each classifier is shown in parentheses.
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AUC values for LA classifiers trained using MHCBN- UPDS, SRDS1, SRDS2,...
- plos.figshare.com
xlsUpdated Jun 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYasser EL-Manzalawy; Drena Dobbs; Vasant Honavar (2023). AUC values for LA classifiers trained using MHCBN- UPDS, SRDS1, SRDS2, SRDS3, and WUPDS datasets and evaluated on the blind test sets of Wang et al. [30]. [Dataset]. http://doi.org/10.1371/journal.pone.0003268.t018xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0003268.t018Dataset updatedJun 1, 2023Dataset provided byPLOS ONEAuthorsYasser EL-Manzalawy; Drena Dobbs; Vasant HonavarLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionAUC values for LA classifiers trained using MHCBN- UPDS, SRDS1, SRDS2, SRDS3, and WUPDS datasets and evaluated on the blind test sets of Wang et al. [30].
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AUC values for the three methods evaluated on MHCBN-SRDS2 datasets.
- plos.figshare.com
xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYasser EL-Manzalawy; Drena Dobbs; Vasant Honavar (2023). AUC values for the three methods evaluated on MHCBN-SRDS2 datasets. [Dataset]. http://doi.org/10.1371/journal.pone.0003268.t013xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0003268.t013Dataset updatedMay 31, 2023Dataset provided byPLOS ONEAuthorsYasser EL-Manzalawy; Drena Dobbs; Vasant HonavarLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionFor each dataset, the rank of each classifier is shown in parentheses.
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AUC values for the three methods evaluated on MHCBN-SRDS1 datasets.
- plos.figshare.com
xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYasser EL-Manzalawy; Drena Dobbs; Vasant Honavar (2023). AUC values for the three methods evaluated on MHCBN-SRDS1 datasets. [Dataset]. http://doi.org/10.1371/journal.pone.0003268.t012xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0003268.t012Dataset updatedMay 31, 2023Dataset provided byPLOS ONEAuthorsYasser EL-Manzalawy; Drena Dobbs; Vasant HonavarLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionFor each dataset, the rank of each classifier is shown in parentheses.
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AUC values for the three methods evaluated on MHCBN-SRDS3 datasets.
- plos.figshare.com
xlsUpdated Jun 4, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYasser EL-Manzalawy; Drena Dobbs; Vasant Honavar (2023). AUC values for the three methods evaluated on MHCBN-SRDS3 datasets. [Dataset]. http://doi.org/10.1371/journal.pone.0003268.t014xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0003268.t014Dataset updatedJun 4, 2023Dataset provided byPLOS ONEAuthorsYasser EL-Manzalawy; Drena Dobbs; Vasant HonavarLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionFor each dataset, the rank of each classifier is shown in parentheses.
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AUC values for CTD classifiers trained using MHCBN- UPDS, SRDS1, SRDS2,...
- plos.figshare.com
xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYasser EL-Manzalawy; Drena Dobbs; Vasant Honavar (2023). AUC values for CTD classifiers trained using MHCBN- UPDS, SRDS1, SRDS2, SRDS3, and WUPDS datasets and evaluated on the blind test sets of Wang et al. [30]. [Dataset]. http://doi.org/10.1371/journal.pone.0003268.t017xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0003268.t017Dataset updatedMay 31, 2023Dataset provided byPLOS ONEAuthorsYasser EL-Manzalawy; Drena Dobbs; Vasant HonavarLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionAUC values for CTD classifiers trained using MHCBN- UPDS, SRDS1, SRDS2, SRDS3, and WUPDS datasets and evaluated on the blind test sets of Wang et al. [30].
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RRID:SCR_007785, nif-0000-03123, MHCBN: A comprehensive database of MHC binding and non-binding peptides (RRID:SCR_007785), MHCBN
The MHCBN is a curated database consisting of detailed information about Major Histocompatibility Complex (MHC) Binding,Non-binding peptides and T-cell epitopes. The version 4.0 of database provides information about peptides interacting with TAP and MHC linked autoimmune diseases.