{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:52:04Z","timestamp":1786121524854,"version":"build-2736575974"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Hybrid insulin peptides (HIPs) are neoepitopes involved in type 1 diabetes (T1D), but their complete repertoire remains unknown. The vast combinatorial space makes experimental screening unfeasible and requires bioinformatics-based prioritization. We developed a multi-level machine learning pipeline for ranking HIP candidates. First, 36 physicochemical and junction-specific features were computed for a reference library of 240 HIPs with known enzyme-linked immunospot (ELISPOT) reactivity, and a baseline Ridge regression model was trained. Next, all possible HIP candidates with 7\u20139 amino acid residues per fragment were generated from eight pancreatic \u03b2-cell secretory granule source proteins, including insulin chains and C-peptide, islet amyloid polypeptide, chromogranin A, neuropeptide Y, and two secretogranins, yielding 1,057,374 candidates. For each source protein, a local weighted XGBoost (Extreme Gradient Boosting) model was trained using Ridge-score-derived pseudo-labels together with weighted ELISPOT-derived and literature-derived reference HIPs. Finally, anchor-calibrated re-ranking was performed in the global model using cosine similarity to positive anchors (n = 46) and negative anchors (n = 210). The Ridge model achieved 5-fold out-of-fold R\u00b2 = 0.711 and an area under the receiver operating characteristic curve (AUC) of 0.967. The global model produced a prioritized list of 40 HIP candidates, five per source protein. The highest ranks were observed for candidates with right fragments from neuropeptide Y, secretogranins 1 and 2, islet amyloid polypeptide, and chromogranin A. Candidates carrying the insulin fragment on the right side were systematically down-ranked, suggesting asymmetry in HIP formation. The proposed pipeline reduces the HIP search space from more than one million sequences to a limited set of candidates for experimental validation and provides a framework adaptable to other chimeric neoepitopes in autoimmunity.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1917213","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T05:27:40Z","timestamp":1786080460000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-level machine learning pipeline for prediction and prioritization of hybrid insulin peptides in type 1 diabetes"],"prefix":"10.3389","volume":"17","author":[{"given":"Ilya","family":"Kandinov","sequence":"first","affiliation":[{"name":"Center for Precision Genetic Technologies for Medicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences","place":["Moscow, 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