{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:20Z","timestamp":1787008460710,"version":"3.56.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072388"],"award-info":[{"award-number":["62072388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Specific Collaborative Fund for Fuzhou-Xiamen-Quanzhou Innovative Technologies and Projects","award":["3502ZCQXT202001"],"award-info":[{"award-number":["3502ZCQXT202001"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702432"],"award-info":[{"award-number":["61702432"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2021J01003"],"award-info":[{"award-number":["2021J01003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision\u2013recall curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code and data are available at: https:\/\/github.com\/galaxysunwen\/MSTE-master.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac094","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T08:42:44Z","timestamp":1644914564000},"page":"2315-2322","source":"Crossref","is-referenced-by-count":35,"title":["Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction"],"prefix":"10.1093","volume":"38","author":[{"given":"Junfeng","family":"Yao","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University , Xiamen, Fujian 361005, China"},{"name":"Institute of Artificial Intelligence, Xiamen University , Xiamen, Fujian 361005, China"},{"name":"Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism, Xiamen University , Xiamen, Fujian 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2131-2452","authenticated-orcid":false,"given":"Wen","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University , Xiamen, Fujian 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongquan","family":"Jian","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University , Xiamen, Fujian 361005, China"},{"name":"Institute of Artificial Intelligence, Xiamen University , Xiamen, Fujian 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7428-5908","authenticated-orcid":false,"given":"Qingqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University , Xiamen, Fujian 361005, China"},{"name":"Institute of Artificial Intelligence, Xiamen University , Xiamen, Fujian 361005, China"},{"name":"Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism, Xiamen University , Xiamen, Fujian 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University , Xiamen, Fujian 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"2023020109031240800_btac094-B1","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.websem.2017.06.002","article-title":"Large-scale structural and textual similarity-based mining of knowledge graph to predict drug\u2013drug interactions","volume":"44","author":"Abdelaziz","year":"2017","journal-title":"J. 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