{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:04:47Z","timestamp":1784750687181,"version":"3.55.0"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region","award":["CityU 11200218"],"award-info":[{"award-number":["CityU 11200218"]}]},{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Special Administrative Region","award":["07181426"],"award-info":[{"award-number":["07181426"]}]},{"DOI":"10.13039\/100017449","name":"Hong Kong Institute for Data Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017449","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["CityU 11202219"],"award-info":[{"award-number":["CityU 11202219"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["CityU 11203520"],"award-info":[{"award-number":["CityU 11203520"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We evaluated the classification part on \u2018DDIs 2013\u2019 dataset and \u2018DTIs\u2019 dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability<\/jats:title><jats:p>Source code are publicly available at https:\/\/github.com\/Layne-Huang\/EGFI.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bib\/bbab451","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T12:21:12Z","timestamp":1635855672000},"source":"Crossref","is-referenced-by-count":36,"title":["EGFI: drug\u2013drug interaction extraction and generation with fusion of enriched entity and sentence information"],"prefix":"10.1093","volume":"23","author":[{"given":"Lei","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong 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