{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:43Z","timestamp":1772138023201,"version":"3.50.1"},"reference-count":84,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease. The code and datasets are publicly available at https:\/\/github.com\/jinyangwu\/KGETCDA.<\/jats:p>","DOI":"10.1093\/bib\/bbad292","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T09:16:53Z","timestamp":1690535813000},"source":"Crossref","is-referenced-by-count":11,"title":["KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations"],"prefix":"10.1093","volume":"24","author":[{"given":"Jinyang","family":"Wu","sequence":"first","affiliation":[{"name":"Xi\u2019an Jiaotong University School of Automation Science and Engineering, , 710049, Shaanxi , China"}]},{"given":"Zhiwei","family":"Ning","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University School of Automation Science and Engineering, , 710049, Shaanxi , China"}]},{"given":"Yidong","family":"Ding","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University School of Automation Science and Engineering, , 710049, Shaanxi , China"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University School of Automation Science and Engineering, , 710049, Shaanxi , China"}]},{"given":"Qinke","family":"Peng","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University School of Automation Science and Engineering, , 710049, Shaanxi , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9086-3982","authenticated-orcid":false,"given":"Laiyi","family":"Fu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University School of Automation Science and Engineering, , 710049, Shaanxi , China"},{"name":"Research Institute of Xi\u2019an Jiaotong University , 311200, Zhejiang , China"},{"name":"Sichuan Digital Economy Industry Development Research Institute , 610036, Sichuan , China"}]}],"member":"286","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"issue":"4","key":"2023092216510957700_ref1","doi-asserted-by":"crossref","first-page":"bbaa350","DOI":"10.1093\/bib\/bbaa350","article-title":"A comprehensive survey on computational methods of non-coding rna and disease association prediction","volume":"22","author":"Lei","year":"2021","journal-title":"Brief Bioinform"},{"issue":"6","key":"2023092216510957700_ref2","doi-asserted-by":"crossref","first-page":"bbab286","DOI":"10.1093\/bib\/bbab286","article-title":"Circular rnas and complex diseases: from experimental results to computational models","volume":"22","author":"Wang","year":"2021","journal-title":"Brief Bioinform"},{"issue":"5","key":"2023092216510957700_ref3","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nbt.2890","article-title":"Detecting and characterizing circular rnas","volume":"32","author":"Jeck","year":"2014","journal-title":"Nat 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