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PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning.

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Title: PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning.
Authors: Le, Phi1, Ung, Leah1, Yang, Hai2, Huang, Anwen1,3, He, Tao4, Bruno, Peter2,5, Oh, David Y1,2, Keenan, Bridget P1,2, Zhang, Li1,2,6
Source: Briefings in Bioinformatics; Jul2025, Vol. 26 Issue 4, p1-11, 11p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 187957664
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=187957664
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      – Type: doi
        Value: 10.1093/bib/bbaf351
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 1
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      – TitleFull: PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning.
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            NameFull: Le, Phi
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            NameFull: Ung, Leah
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            NameFull: Yang, Hai
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            NameFull: He, Tao
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            NameFull: Bruno, Peter
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 26
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            – TitleFull: Briefings in Bioinformatics
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