{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:36:42Z","timestamp":1774723002602,"version":"3.50.1"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T00:00:00Z","timestamp":1724371200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012547","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2023JJA170019"],"award-info":[{"award-number":["2023JJA170019"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022LZL003"],"award-info":[{"award-number":["ZR2022LZL003"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Numerous studies have demonstrated that microRNAs (miRNAs) are critically important for the prediction, diagnosis, and characterization of diseases. However, identifying miRNA\u2013disease associations through traditional biological experiments is both costly and time-consuming. To further explore these associations, we proposed a model based on hybrid high-order moments combined with element-level attention mechanisms (HHOMR). This model innovatively fused hybrid higher-order statistical information along with structural and community information. Specifically, we first constructed a heterogeneous graph based on existing associations between miRNAs and diseases. HHOMR employs a structural fusion layer to capture structure-level embeddings and leverages a hybrid high-order moments encoder layer to enhance features. Element-level attention mechanisms are then used to adaptively integrate the features of these hybrid moments. Finally, a multi-layer perceptron is utilized to calculate the association scores between miRNAs and diseases. Through five-fold cross-validation on HMDD v2.0, we achieved a mean AUC of 93.28%. Compared with four state-of-the-art models, HHOMR exhibited superior performance. Additionally, case studies on three diseases\u2014esophageal neoplasms, lymphoma, and prostate neoplasms\u2014were conducted. Among the top 50 miRNAs with high disease association scores, 46, 47, and 45 associated with these diseases were confirmed by the dbDEMC and miR2Disease databases, respectively. Our results demonstrate that HHOMR not only outperforms existing models but also shows significant potential in predicting miRNA\u2013disease associations.<\/jats:p>","DOI":"10.1093\/bib\/bbae412","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T04:33:26Z","timestamp":1724387606000},"source":"Crossref","is-referenced-by-count":9,"title":["HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction"],"prefix":"10.1093","volume":"25","author":[{"given":"Zhengwei","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology , China University of Mining and Technology, Xuzhou 221116,","place":["China"]},{"name":"Guangxi Academy of Science , Nanning, 530007,","place":["China"]},{"name":"School of Information Science and Engineering , Zaozhuang University, Zaozhuang, 277160,","place":["China"]}]},{"given":"Lipeng","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , China University of Mining and Technology, Xuzhou 221116,","place":["China"]}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , China University of Mining and Technology, Xuzhou 221116,","place":["China"]},{"name":"Guangxi Academy of Science , Nanning, 530007,","place":["China"]}]},{"given":"Wenjing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , China University of Mining and Technology, Xuzhou 221116,","place":["China"]}]},{"given":"Ru","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , China University of Mining and Technology, Xuzhou 221116,","place":["China"]},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education , China University of Mining and Technology, Xuzhou 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