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Accurate prediction of TCR\u2013epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR\u2013epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR\u2013epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR\u2013epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https:\/\/github.com\/uta-smile\/GTE.<\/jats:p>","DOI":"10.1093\/bib\/bbae343","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:51:38Z","timestamp":1721044298000},"source":"Crossref","is-referenced-by-count":17,"title":["GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity"],"prefix":"10.1093","volume":"25","author":[{"given":"Feng","family":"Jiang","sequence":"first","affiliation":[{"name":"University of Texas at Arlington Department of Computer Science and Engineering, , 701 S. Nedderman Drive, TX 76019, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhi","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington Department of Computer Science and Engineering, , 701 S. Nedderman Drive, TX 76019, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hehuan","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington Department of Computer Science and Engineering, , 701 S. Nedderman Drive, TX 76019, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saiyang","family":"Na","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington Department of Computer Science and Engineering, , 701 S. Nedderman Drive, TX 76019, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenliang","family":"Zhong","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington Department of Computer Science and Engineering, , 701 S. Nedderman Drive, TX 76019, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Han","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center Public Health, , 5323 Harry Hines Blvd, TX 75390, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center Public Health, , 5323 Harry Hines Blvd, TX 75390, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junzhou","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington Department of Computer Science and Engineering, , 701 S. 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