{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:28:47Z","timestamp":1773246527337,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T00:00:00Z","timestamp":1615680000000},"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\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"crossref","award":["2017R1E1A1A03070345"],"award-info":[{"award-number":["2017R1E1A1A03070345"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"crossref","award":["21182MFDS265"],"award-info":[{"award-number":["21182MFDS265"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Polypharmacy side effects should be carefully considered for new drug development. However, considering all the complex drug\u2013drug interactions that cause polypharmacy side effects is challenging. Recently, graph neural network (GNN) models have handled these complex interactions successfully and shown great predictive performance. Nevertheless, the GNN models have difficulty providing intelligible factors of the prediction for biomedical and pharmaceutical domain experts.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Method<\/jats:title>\n                  <jats:p>A novel approach, graph feature attention network (GFAN), is presented for interpretable prediction of polypharmacy side effects by emphasizing target genes differently. To artificially simulate polypharmacy situations, where two different drugs are taken together, we formulated a node classification problem by using the concept of line graph in graph theory.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Experiments with benchmark datasets validated interpretability of the GFAN and demonstrated competitive performance with the graph attention network in a previous work. And the specific cases in the polypharmacy side-effect prediction experiments showed that the GFAN model is capable of very sensitively extracting the target genes for each side-effect prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/SunjooBang\/Polypharmacy-side-effect-prediction.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab174","type":"journal-article","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T12:16:55Z","timestamp":1615551415000},"page":"2955-2962","source":"Crossref","is-referenced-by-count":25,"title":["Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6986-3993","authenticated-orcid":false,"given":"Sunjoo","family":"Bang","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Ajou University , Suwon 443-749, South Korea"}]},{"given":"Jong Ho","family":"Jhee","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Ajou University , Suwon 443-749, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8347-8277","authenticated-orcid":false,"given":"Hyunjung","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Ajou University , Suwon 443-749, South Korea"}]}],"member":"286","published-online":{"date-parts":[[2021,3,14]]},"reference":[{"key":"2023061310553423300_btab174-B2","author":"Bahdanau","year":"2014"},{"key":"2023061310553423300_btab174-B3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.gpb.2017.07.003","article-title":"Deep learning and its applications in biomedicine","volume":"16","author":"Cao","year":"2018","journal-title":"Genomics Proteomics Bioinf"},{"key":"2023061310553423300_btab174-B4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1038\/nrd2005","article-title":"Drug therapy of the metabolic syndrome: minimizing the emerging crisis in polypharmacy","volume":"5","author":"Grundy","year":"2006","journal-title":"Nat. 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