{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T09:30:22Z","timestamp":1780997422921,"version":"3.54.1"},"reference-count":82,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972399"],"award-info":[{"award-number":["61972399"]}],"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":["11931008"],"award-info":[{"award-number":["11931008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Adverse drug\u2013drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs\u2019 extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs\u2019 KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI.<\/jats:p>","DOI":"10.1093\/bib\/bbad215","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T04:50:44Z","timestamp":1686286244000},"source":"Crossref","is-referenced-by-count":59,"title":["MCFF-MTDDI: multi-channel feature fusion for multi-typed drug\u2013drug interaction prediction"],"prefix":"10.1093","volume":"24","author":[{"given":"Chen-Di","family":"Han","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology , Xuzhou, 221116 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6795-4007","authenticated-orcid":false,"given":"Chun-Chun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology , Xuzhou, 221116 , China"},{"name":"School of Science, Jiangnan University , Wuxi, 214122 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[{"name":"The Future Laboratory, Tsinghua University , Beijing, 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2086-8724","authenticated-orcid":false,"given":"Xing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology , Xuzhou, 221116 , China"},{"name":"School of Science, Jiangnan University , Wuxi, 214122 , China"},{"name":"Artificial Intelligence Research Institute, China University of Mining and Technology , Xuzhou, 221116 , 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