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dc.contributor.authorCao, J. R.
dc.contributor.authorvan Veen, E. M.
dc.contributor.authorPeek, N.
dc.contributor.authorRenehan, Andrew G
dc.contributor.authorAnaniadou, S.
dc.date.accessioned2023-04-19T09:32:52Z
dc.date.available2023-04-19T09:32:52Z
dc.date.issued2023en
dc.identifier.citationCao JR, van Veen EM, Peek N, Renehan AG, Ananiadou S. A Novel Automated Approach to Mutation-Cancer Relation Extraction by Incorporating Heterogeneous Knowledge. Ieee Journal of Biomedical and Health Informatics. 2023 Feb;27(2):1096-105. PubMed PMID: WOS:000943693600051.en
dc.identifier.pmid36395134en
dc.identifier.doi10.1109/JBHI.2022.3220924.en
dc.identifier.urihttp://hdl.handle.net/10541/626215
dc.description.abstractAutomatic extraction of relations between gene mutations and cancer entities occurring in the cancer literature using text mining can rapidly provide vital information to support precision cancer medicine. However, mutation-cancer relation extraction is more challenging than general relation extraction from free text, since it is often not possible without cancer-specific background knowledge and thus the model replies on a deeper understanding of complex surrounding tokens. We propose a deep learning model that jointly extracts mutations and their associated cancers. Background knowledge comes from two different knowledge bases which store different types of information about mutations. Given the different ways in which knowledge is stored in these two resources, we propose two separate methods for embedding knowledge, namely sentence-based knowledge integration and attribute-aware knowledge integration. The evaluation demonstrated that our model outperforms a number of baseline models and gains 96.00%, 92.57% and 94.57% F1 scores on three public datasets, EMU BCa, EMU PCa, and BRONCO, thus illustrating the effectiveness of our knowledge integration approach. The auxiliary experiments show that our models can utilize more informative text from the KBs and link the mutations to their corresponding cancer disease although the input text provides insufficient context.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.1109/JBHI.2022.3220924.en
dc.titleA novel automated approach to mutation-cancer relation extraction by incorporating heterogeneous knowledgeen
dc.typeArticleen
dc.contributor.departmentDepartment of Computer Science, The University of Manchester, Manchesteren
dc.identifier.journalIeee Journal of Biomedical and Health Informaticsen
dc.description.noteen]


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