{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T05:59:31Z","timestamp":1793426371970,"version":"4.5.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T00:00:00Z","timestamp":1590278400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T00:00:00Z","timestamp":1590278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Fundamental Theory and Applications of Big Data with Knowledge Engineering","award":["2016YFB1000903"],"award-info":[{"award-number":["2016YFB1000903"]}]},{"name":"Ministry of Education-Research Foundation of China Mobile Communication Corp","award":["MCM20160404"],"award-info":[{"award-number":["MCM20160404"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-020-3540-8","type":"journal-article","created":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T08:02:32Z","timestamp":1590307352000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Bio-semantic relation extraction with attention-based external knowledge reinforcement"],"prefix":"10.1186","volume":"21","author":[{"given":"Zhijing","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Lian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyong","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangrong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2736-6438","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,5,24]]},"reference":[{"key":"3540_CR1","volume-title":"Encyclopedia of Systems Biology","author":"V Chelliah","year":"2013","unstructured":"Chelliah V, Laibe C, Nov\u00e8re NL. BioModels database: a repository of mathematical models of biological processes. In: Encyclopedia of Systems Biology. New York, NY: Springer; 2013."},{"issue":"8","key":"3540_CR2","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1186\/1471-2105-12-S8-S3","volume":"12","author":"M Krallinger","year":"2011","unstructured":"Krallinger M, Vazquez M, Leitner F, Salgado D, Chatr-Aryamontri A, Winter A, Perfetto L, Briganti L, Licata L, Iannuccelli M, Castagnoli L, Cesareni G, Tyers M, Schneider G, Rinaldi F, Leaman R, Gonzalez G, Matos S, Kim S, Wilbur WJ, Rocha L, Shatkay H, Tendulkar AV, Agarwal S, Liu F, Wang X, Rak R, Noto K, Elkan C, Lu Z, Dogan RI, Fontaine JF, Andrade-Navarro MA, Valencia A. The protein-protein interaction tasks of BioCreative III: classification\/ranking of articles and linking bio-ontology concepts to full text. BMC Bioinformatics. 2011;12(8):S3.","journal-title":"BMC Bioinformatics"},{"key":"3540_CR3","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.cell.2005.08.029","volume":"122","author":"U Stelzl","year":"2015","unstructured":"Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, Timm J, Mintzlaff S, Abraham C, Bock N, Kietzmann S, Goedde A, Toks\u00f6z E, Droege A, Krobitsch S, Korn B, Birchmeier W, Lehrach H, Wanker EE. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2015;122:957\u201368. https:\/\/doi.org\/10.1016\/j.cell.2005.08.029.","journal-title":"Cell"},{"key":"3540_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/8479587","volume":"2016","author":"Lei Hua","year":"2016","unstructured":"Hua L, Quan C. A shortest dependency path based convolutional neural network for protein-protein relation extraction. Biomed Res Int. 2016. https:\/\/doi.org\/10.1155\/2016\/8479587.","journal-title":"BioMed Research International"},{"issue":"Suppl 8","key":"3540_CR5","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1186\/1471-2105-12-S8-S1","volume":"12","author":"CN Arighi","year":"2011","unstructured":"Arighi CN, Lu Z, Krallinger M, Cohen KB, Wilbur WJ, Valencia A, Hirschman L, Wu CH. Overview of the BioCreative III workshop. BMC Bioinformatics. 2011;12(Suppl 8):S1.","journal-title":"BMC Bioinformatics"},{"issue":"Suppl 1","key":"3540_CR6","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1186\/1471-2105-6-S1-S1","volume":"6","author":"L Hirschman","year":"2005","unstructured":"Hirschman L, Yeh A, Blaschke C, Valencia A. Overview of BioCreAtIvE: Critical assessment of information extraction for biology. BMC Bioinformatics. 2005;6(Suppl 1):S1. https:\/\/doi.org\/10.1186\/1471-2105-6-S1-S1.","journal-title":"BMC Bioinformatics"},{"key":"3540_CR7","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1006\/jmbi.2000.4092","volume":"302","author":"X Gallet","year":"2000","unstructured":"Gallet X, Charloteaux B, Thomas A, Brasseur R. A fast method to predict protein interaction sites from sequences. J Mol Biol. 2000;302:917\u201326.","journal-title":"J Mol Biol"},{"key":"3540_CR8","doi-asserted-by":"crossref","unstructured":"Yan C, Dobbs D, Honavar V. Identification of surface residues involved in protein-protein interaction-a support vector machine approach. Proc Conf Intell Syst Des Appl. 2003:53\u201362.","DOI":"10.1007\/978-3-540-44999-7_6"},{"key":"3540_CR9","doi-asserted-by":"crossref","unstructured":"Phan HTT, Stemberg MJE, Gelenbe E. Aligning protein-protein interaction networks using random neural networks. IEEE Int Conf Bioinformatics Biomedicine. 2012:1\u20136.","DOI":"10.1109\/BIBM.2012.6392664"},{"issue":"1","key":"3540_CR10","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1186\/s12859-017-1700-2","volume":"18","author":"T Sun","year":"2017","unstructured":"Sun T, Zhou B, Lai L, Pei J. Sequence-based prediction of protein protein interaction using a deep-learning algorithm. Bmc Bioinformatics. 2017;18(1):277.","journal-title":"Bmc Bioinformatics"},{"key":"3540_CR11","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1186\/s12859-017-1609-9","volume":"18","author":"F Li","year":"2017","unstructured":"Li F, Zhang M, Guohong F, Ji D. A neural joint model for entity and relation extraction from biomedical text. BMC Bioinformatics. 2017;18:198.","journal-title":"BMC Bioinformatics"},{"key":"3540_CR12","unstructured":"Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN. Convolutional Sequence to Sequence Learning. Proceedings of the 34th International Conference on Machine Learning. JMLR.org. 2017;70:1243\u201352."},{"key":"3540_CR13","unstructured":"Ashish Vaswani et al. Attention is all you need. 2017."},{"key":"3540_CR14","doi-asserted-by":"crossref","unstructured":"Lin YK, Shen SQ, Liu ZY, Luan HB, Sun MS. Neural relation extraction with selective attention over instances. Proc 54th Ann Meet Assoc Comput Linguist. 2016;1:2124\u201333.","DOI":"10.18653\/v1\/P16-1200"},{"key":"3540_CR15","doi-asserted-by":"crossref","unstructured":"Verga P, Strubell E, Shai O, Mccallum A. Attending to all mention pairs for full abstract biological relation extraction. 2017.","DOI":"10.18653\/v1\/N18-1080"},{"key":"3540_CR16","doi-asserted-by":"crossref","unstructured":"Zhou P, Shi W, Tian J, Qi ZY, Li BC, Hao HW, Xu B. Attention-based bidirectional long short-term memory networks for relation classification. Meet Assoc Comput Linguist. 2016:207\u201312.","DOI":"10.18653\/v1\/P16-2034"},{"key":"3540_CR17","doi-asserted-by":"crossref","unstructured":"Perera S, Sheth A, Thirunarayan K, Nair S, Shah N. Challenges in understanding clinical notes: why nlp engines fall short and where background knowledge can help. Proc 2013 Int Workshop Data Manage Analytics Healthcare. 2013:21\u20136.","DOI":"10.1145\/2512410.2512427"},{"key":"3540_CR18","doi-asserted-by":"crossref","unstructured":"Yang B, Mitchell T. Leveraging knowledge bases in LSTMs for improving machine Reading. Proc 55th Ann Meet Assoc Comput Linguist. 2017;1:1436\u201346.","DOI":"10.18653\/v1\/P17-1132"},{"key":"3540_CR19","doi-asserted-by":"crossref","unstructured":"Zhou HW, Liu Z, et al. Leveraging prior knowledge for protein-protein interaction extraction with memory network. Database. 2018:1\u201313.","DOI":"10.1093\/database\/bay071"},{"key":"3540_CR20","doi-asserted-by":"crossref","unstructured":"Zhou H, Yang Y, Ning S, Liu Z, Lang C, Lin YY, Huang DG. Combining context and knowledge representations for chemical-disease relation extraction. IEEE\/ACM Trans Comput Biol Bioinformatics. 2018;16:1879-89.","DOI":"10.1109\/TCBB.2018.2838661"},{"key":"3540_CR21","doi-asserted-by":"crossref","unstructured":"Asada M, Miwa M, Sasaki Y. Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information. Proc 56th Ann Meet Assoc Comput Linguist. 2018;2:680\u20135.","DOI":"10.18653\/v1\/P18-2108"},{"key":"3540_CR22","doi-asserted-by":"publisher","unstructured":"Peng Y, Rios A, Kavuluru R, Lu Z. Extracting chemical\u2013protein relations with ensembles of SVM and deep learning models. Database. 2018. https:\/\/doi.org\/10.1093\/database\/bay073.","DOI":"10.1093\/database\/bay073"},{"key":"3540_CR23","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.jbi.2018.03.011","volume":"81","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Lin H, Yang Z, Wang J, Zhang S, Sun Y, Yang L. A hybrid model based on neural networks for biomedical relation extraction. J Biomed Inform. 2018;81:83\u201392.","journal-title":"J Biomed Inform"},{"key":"3540_CR24","unstructured":"Zeiler MD. ADADELTA: an adaptive learning rate method. [Online]. Available: https:\/\/arxiv.org\/abs\/1212.5701. Volume abs\/1212.5701."},{"key":"3540_CR25","unstructured":"Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012:281\u2013305."},{"key":"3540_CR26","doi-asserted-by":"crossref","unstructured":"Li C, Rao ZQ, Zhang XR. LitWay, discriminative extraction for different bio-events. Bionlp Shared Task Workshop. 2016:32\u201341.","DOI":"10.18653\/v1\/W16-3004"},{"key":"3540_CR27","doi-asserted-by":"crossref","unstructured":"He XY, Li LS, Zheng JQ, Qin MY. Extracting Biomedical Event Using Feature Selection and Word Representation. Proc 4th BioNLP Shared Task Workshop. 2016:101.","DOI":"10.18653\/v1\/W16-3013"},{"key":"3540_CR28","first-page":"103","volume-title":"Document triage and relation extraction for protein protein interactions affected by mutations","author":"QY Chen","year":"2017","unstructured":"Chen QY, Panyam NC, Elangovan A, Davis M, Karin V. Document triage and relation extraction for protein protein interactions affected by mutations. Washington, DC: Proceedings of the 2017 Workshop on BioCreatice VI; 2017. p. 103\u20136."},{"key":"3540_CR29","unstructured":"Tran T, Kavuluru R. Exploring a deep learning pipeline for the BioCreative VI precision medicine task. Proceedings of the 2017 Workshop on BioCreatice VI. 2017:107\u2013110."},{"key":"3540_CR30","first-page":"837","volume":"10","author":"TF Schultz","year":"1998","unstructured":"Schultz TF, Medina J, Hill A, Quatrano RS. 14-3-3 proteins are part of an abscisic acid-VIVIPAROUS1 (VP1) response complex in the Em promoter and interact with VP1 and EmBP1. Plant Cell. 1998;10:837\u201347.","journal-title":"Plant Cell"},{"issue":"4","key":"3540_CR31","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/BF00029604","volume":"25","author":"Guihua Lu","year":"1994","unstructured":"Lu G, de Vetten NC, Sehnke PC, Isobe T, Ichimura T, Fu H, van Heusden GP, Ferl RJ. A single Arabidopsis GF14 isoform possesses biochemical characteristics of diverse 14\u20133-3 homologues. Plant Mol Biol. 1994;25:659\u201367. https:\/\/doi.org\/10.1007\/BF00029604.","journal-title":"Plant Molecular Biology"},{"key":"3540_CR32","doi-asserted-by":"crossref","unstructured":"Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D. The stanford coreNLP natural language processing toolkit. Proc 52nd Ann Meet Assoc Comput Linguist: Syst Demonstrations. 2014:55\u201360.","DOI":"10.3115\/v1\/P14-5010"},{"key":"3540_CR33","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In: proceedings of workshop at ICLR. arXiv. 2013:1301.3781."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3540-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-020-3540-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3540-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T07:59:49Z","timestamp":1722931189000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-3540-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,24]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3540"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-3540-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,24]]},"assertion":[{"value":"20 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no conflict of interest related to the research presented herein.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"213"}}