{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T11:57:09Z","timestamp":1775908629792,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&D Plan of Heilongjiang Province","award":["2022ZX02C21"],"award-info":[{"award-number":["2022ZX02C21"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102116"],"award-info":[{"award-number":["62102116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA\u2013disease interactions. Although there have been computational methods proposed for the detection of piRNA\u2013disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA\u2013disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA\u2013disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA\u2013disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https:\/\/github.com\/chenqiuhao\/PUTransGCN<\/jats:p>","DOI":"10.1093\/bib\/bbae144","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T13:49:31Z","timestamp":1712411371000},"source":"Crossref","is-referenced-by-count":11,"title":["PUTransGCN: identification of piRNA\u2013disease associations based on attention encoding graph convolutional network and positive unlabelled learning"],"prefix":"10.1093","volume":"25","author":[{"given":"Qiuhao","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Bioinformatics, Harbin Institute of Technology , 150000, Harbin, Heilongjiang , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , 150000, Harbin, Heilongjiang , 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