{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T21:11:37Z","timestamp":1780521097875,"version":"3.54.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2021,2,11]],"date-time":"2021-02-11T00:00:00Z","timestamp":1613001600000},"content-version":"vor","delay-in-days":41,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976147; 2017YFB1002101"],"award-info":[{"award-number":["61976147; 2017YFB1002101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001839","name":"UGC","doi-asserted-by":"publisher","award":["PolyU YW4H"],"award-info":[{"award-number":["PolyU YW4H"]}],"id":[{"id":"10.13039\/501100001839","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,2,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form\u2014SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively.<\/jats:p><jats:p>Database URL: https:\/\/github.com\/grapeff\/SBEL_datasets<\/jats:p>","DOI":"10.1093\/database\/baab005","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T12:10:39Z","timestamp":1611663039000},"source":"Crossref","is-referenced-by-count":10,"title":["Extraction of causal relations based on SBEL and BERT model"],"prefix":"10.1093","volume":"2021","author":[{"given":"Yifan","family":"Shao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu Province, China, 215006"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoru","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu Province, China, 215006"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghang","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Chinese & Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China, 999077"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longhua","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu Province, China, 215006"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu Province, China, 215006"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"key":"2021022002280676700_R1","first-page":"61","article-title":"Learning relations from biomedical corpora using dependency trees","author":"Katrenko","year":"2006"},{"key":"2021022002280676700_R2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1504\/IJDMB.2016.076534","article-title":"A protein-protein interaction extraction approach based on deep neural network","volume":"15","author":"Zhao","year":"2016","journal-title":"Int. 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