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- Publisher Website: 10.1007/978-3-030-17083-7_17
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Conference Paper: RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature
Title | RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature |
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Authors | |
Keywords | Deep learning Gene-disease association Literature mining Relation Extraction |
Issue Date | 2019 |
Publisher | Springer. |
Citation | 23rd Annual International Conference on Research in Computational Molecular Biology (RECOMB 2019), Washington, DC, USA, 5-8 May 2019. Conference proceedings in Lenore J. Cowen (ed.), Research in Computational Molecular Biology, p. 272-284. Cham: Springer, 2019 How to Cite? |
Abstract | Over one million new biomedical articles are published every year. Efficient and accurate text-mining tools are urgently needed to automatically extract knowledge from these articles to support research and genetic testing. In particular, the extraction of gene-disease associations is mostly studied. However, existing text-mining tools for extracting gene-disease associations have limited capacity, as each sentence is considered separately. Our experiments show that the best existing tools, such as BeFree and DTMiner, achieve a precision of 48% and recall rate of 78% at most. In this study, we designed and implemented a deep learning approach, named RENET, which considers the correlation between the sentences in an article to extract gene-disease associations. Our method has significantly improved the precision and recall rate to 85.2% and 81.8%, respectively. The source code of RENET is available at https://bitbucket.org/alexwuhkucs/gda-extraction/src/master/. |
Persistent Identifier | http://hdl.handle.net/10722/274108 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
Series/Report no. | Lecture Notes in Computer Science (LNCS) ; v. 11467 |
DC Field | Value | Language |
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dc.contributor.author | Wu, Y | - |
dc.contributor.author | Luo, R | - |
dc.contributor.author | Leung, HCM | - |
dc.contributor.author | Ting, HF | - |
dc.contributor.author | Lam, TW | - |
dc.date.accessioned | 2019-08-18T14:55:14Z | - |
dc.date.available | 2019-08-18T14:55:14Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | 23rd Annual International Conference on Research in Computational Molecular Biology (RECOMB 2019), Washington, DC, USA, 5-8 May 2019. Conference proceedings in Lenore J. Cowen (ed.), Research in Computational Molecular Biology, p. 272-284. Cham: Springer, 2019 | - |
dc.identifier.isbn | 978-3-030-17082-0 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/274108 | - |
dc.description.abstract | Over one million new biomedical articles are published every year. Efficient and accurate text-mining tools are urgently needed to automatically extract knowledge from these articles to support research and genetic testing. In particular, the extraction of gene-disease associations is mostly studied. However, existing text-mining tools for extracting gene-disease associations have limited capacity, as each sentence is considered separately. Our experiments show that the best existing tools, such as BeFree and DTMiner, achieve a precision of 48% and recall rate of 78% at most. In this study, we designed and implemented a deep learning approach, named RENET, which considers the correlation between the sentences in an article to extract gene-disease associations. Our method has significantly improved the precision and recall rate to 85.2% and 81.8%, respectively. The source code of RENET is available at https://bitbucket.org/alexwuhkucs/gda-extraction/src/master/. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Research in Computational Molecular Biology | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science (LNCS) ; v. 11467 | - |
dc.subject | Deep learning | - |
dc.subject | Gene-disease association | - |
dc.subject | Literature mining | - |
dc.subject | Relation Extraction | - |
dc.title | RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Luo, R: rbluo@cs.hku.hk | - |
dc.identifier.email | Leung, HCM: cmleung3@hku.hk | - |
dc.identifier.email | Ting, HF: hfting@cs.hku.hk | - |
dc.identifier.email | Lam, TW: twlam@cs.hku.hk | - |
dc.identifier.authority | Luo, R=rp02360 | - |
dc.identifier.authority | Leung, HCM=rp00144 | - |
dc.identifier.authority | Ting, HF=rp00177 | - |
dc.identifier.authority | Lam, TW=rp00135 | - |
dc.identifier.doi | 10.1007/978-3-030-17083-7_17 | - |
dc.identifier.scopus | eid_2-s2.0-85065526380 | - |
dc.identifier.hkuros | 302240 | - |
dc.identifier.volume | 2019 | - |
dc.identifier.spage | 272 | - |
dc.identifier.epage | 284 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.publisher.place | Cham | - |
dc.identifier.issnl | 0302-9743 | - |