{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T10:42:50Z","timestamp":1770547370350,"version":"3.49.0"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":50,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373080"],"award-info":[{"award-number":["62373080"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62131004"],"award-info":[{"award-number":["62131004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62450002"],"award-info":[{"award-number":["62450002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62425107"],"award-info":[{"award-number":["62425107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Tianfu Emei Plan and Special Support Plan for High level Talents in Zhejiang Province","award":["2021R52019"],"award-info":[{"award-number":["2021R52019"]}]},{"name":"science and technology innovation Program of Hunan Province","award":["2024RC4013"],"award-info":[{"award-number":["2024RC4013"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022ZD0117700"],"award-info":[{"award-number":["2022ZD0117700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Microorganisms inhabit various regions of the human body and significantly contribute to numerous diseases. Predicting the associations between microbes and diseases is crucial for understanding pathogenic mechanisms and informing prevention and treatment strategies. Biological experiments to determine these associations are time-consuming and costly. Therefore, integrating deep learning with biological networks can efficiently identify potential microbe-disease associations on a large scale.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We propose an adversarial regularized autoencoder graph neural network algorithm, named Stacked Adversarial Regularization for Microbe-Disease Associations Prediction (SARMDA), for predicting associations between microbes and diseases. First, we integrate topological structural similarity and functional similarity metrics of microbes and diseases to construct a heterogeneous network. Then, utilizing an autoencoder based on GraphSAGE, we learn both the topological and attribute representations of nodes within the constructed network. Finally, we introduce an adversarial regularized autoencoder graph neural network embedding model to address the inherent limitations of traditional GraphSAGE autoencoders in capturing global information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Under the five-fold cross-validation on microbe-disease pairs, SARMDA was compared with eight advanced methods using the Human Microbe-Disease Association Database (HMDAD) and Disbiome databases. The best area under the ROC curve (AUC) achieved by SARMDA on HMDAD was 0.9891$\\pm$0.0057, and the best area under the precision-recall curve (AUPR) was 0.9902$\\pm$0.0128. On the Disbiome dataset, the AUC was 0.9328$\\pm$0.0072, and the best AUPR was 0.9233$\\pm$0.0089, outperforming the other eight MDAs prediction methods. Furthermore, the effectiveness of our model was demonstrated through a detailed analysis of asthma and inflammatory bowel disease cases.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbae584","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T01:31:13Z","timestamp":1731375073000},"source":"Crossref","is-referenced-by-count":4,"title":["Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3408-5375","authenticated-orcid":false,"given":"Limuxuan","family":"He","sequence":"first","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences , University of Electronic Science and Technology of China, Qingshuihe Campus, 2006 Xiyuan Avenue, West District, High-tech Zone, Chengdu, Sichuan 610054,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences , University of Electronic Science and Technology of China, Qingshuihe Campus, 2006 Xiyuan Avenue, West District, High-tech Zone, Chengdu, Sichuan 610054,","place":["China"]},{"name":"School of Information Technology and Administration , Hunan University of Finance and Economics, 139, 2nd Fenglin Road, Yuelu District, Changsha, Hunan 410205,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Life Science and Medicine , Zhejiang Sci-Tech University, No. 5 Second Avenue, Xiasha Higher Education Zone, Hangzhou, Zhejiang 310018,","place":["PR China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Materials , China Academy of Engineering Physics, Huafeng Xincun No. 9, Jiangyou, Mianyang, Sichuan 621907,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yansu","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences , University of Electronic Science and Technology of China, Qingshuihe Campus, 2006 Xiyuan Avenue, West District, High-tech Zone, Chengdu, Sichuan 610054,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"2024111201305994900_ref1","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1016\/j.bbadis.2014.05.023","article-title":"Rapidly expanding knowledge on the role of the gut microbiome in health and 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