{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:41:35Z","timestamp":1784248895683,"version":"3.55.0"},"reference-count":71,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["2021D01D05"],"award-info":[{"award-number":["2021D01D05"]}]},{"name":"Pioneer Hundred Talents Program of Chinese Academy of Sciences"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172355"],"award-info":[{"award-number":["62172355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSFC Excellent Young Scholars Program","award":["61722212"],"award-info":[{"award-number":["61722212"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug\u2013drug interactions (DDIs) are known as the main cause of life-threatening adverse events, and their identification is a key task in drug development. Existing computational algorithms mainly solve this problem by using advanced representation learning techniques. Though effective, few of them are capable of performing their tasks on biomedical knowledge graphs (KGs) that provide more detailed information about drug attributes and drug-related triple facts. In this work, an attention-based KG representation learning framework, namely DDKG, is proposed to fully utilize the information of KGs for improved performance of DDI prediction. In particular, DDKG first initializes the representations of drugs with their embeddings derived from drug attributes with an encoder\u2013decoder layer, and then learns the representations of drugs by recursively propagating and aggregating first-order neighboring information along top-ranked network paths determined by neighboring node embeddings and triple facts. Last, DDKG estimates the probability of being interacting for pairwise drugs with their representations in an end-to-end manner. To evaluate the effectiveness of DDKG, extensive experiments have been conducted on two practical datasets with different sizes, and the results demonstrate that DDKG is superior to state-of-the-art algorithms on the DDI prediction task in terms of different evaluation metrics across all datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbac140","type":"journal-article","created":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T19:17:38Z","timestamp":1648495058000},"source":"Crossref","is-referenced-by-count":110,"title":["Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions"],"prefix":"10.1093","volume":"23","author":[{"given":"Xiaorui","family":"Su","sequence":"first","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Laboratory of Minority Speech and Language Information 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