Large-scale event extraction from literature with multi-level gene normalization.
Identifieur interne : 000044 ( PubMed/Corpus ); précédent : 000043; suivant : 000045Large-scale event extraction from literature with multi-level gene normalization.
Auteurs : Sofie Van Landeghem ; Jari Björne ; Chih-Hsuan Wei ; Kai Hakala ; Sampo Pyysalo ; Sophia Ananiadou ; Hung-Yu Kao ; Zhiyong Lu ; Tapio Salakoski ; Yves Van De Peer ; Filip GinterSource :
- PloS one [ 1932-6203 ] ; 2013.
English descriptors
- KwdEn :
- MESH :
Abstract
Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons - Attribution - Share Alike (CC BY-SA) license.
DOI: 10.1371/journal.pone.0055814
PubMed: 23613707
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To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons - Attribution - Share Alike (CC BY-SA) license.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23613707</PMID><DateCreated><Year>2013</Year><Month>4</Month><Day>24</Day></DateCreated><DateCompleted><Year>2013</Year><Month>11</Month><Day>11</Day></DateCompleted><DateRevised><Year>2016</Year><Month>11</Month><Day>7</Day></DateRevised><Article PubModel="Electronic-Print"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><Issue>4</Issue><PubDate><Year>2013</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Large-scale event extraction from literature with multi-level gene normalization.</ArticleTitle><Pagination><MedlinePgn>e55814</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0055814</ELocationID><Abstract><AbstractText>Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). 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Kingdom</Country></Grant><Grant><Agency>Biotechnology and Biological Sciences Research Council</Agency><Country>United Kingdom</Country></Grant><Grant><Agency>Intramural NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D057666">Research Support, American Recovery and Reinvestment Act</PublicationType><PublicationType UI="D052060">Research Support, N.I.H., Intramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>Apr</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2000 Jan 1;28(1):27-30</RefSource><PMID Version="1">10592173</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2012;13:161</RefSource><PMID Version="1">22776079</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nat Genet. 2004 Jul;36(7):664</RefSource><PMID Version="1">15226743</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2005 Jan 15;21(2):248-56</RefSource><PMID Version="1">15333458</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2005;6 Suppl 1:S1</RefSource><PMID Version="1">15960821</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2005;6 Suppl 1:S3</RefSource><PMID Version="1">15960837</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2007 Jan 15;23(2):e237-44</RefSource><PMID Version="1">17237098</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2007 Aug 15;23(16):2196-7</RefSource><PMID Version="1">17545178</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2008 Jan;36(Database issue):D842-6</RefSource><PMID Version="1">17932060</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Pac Symp Biocomput. 2008;:652-63</RefSource><PMID Version="1">18229723</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2008 Jul 1;24(13):i286-94</RefSource><PMID Version="1">18586726</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genome Biol. 2008;9 Suppl 2:S1</RefSource><PMID Version="1">18834487</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2010 Jan;38(Database issue):D563-9</RefSource><PMID Version="1">19884133</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Biomed Inform. 2010 Apr;43(2):173-89</RefSource><PMID Version="1">19900574</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Trends Biotechnol. 2010 Jul;28(7):381-90</RefSource><PMID Version="1">20570001</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE/ACM Trans Comput Biol Bioinform. 2010 Jul-Sep;7(3):385-99</RefSource><PMID Version="1">20704011</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nat Biotechnol. 2010 Sep;28(9):935-42</RefSource><PMID Version="1">20829833</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2010;11:492</RefSource><PMID Version="1">20920264</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2011 Jan;39(Database issue):D392-401</RefSource><PMID Version="1">21036868</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2011 Jan;39(Database issue):D800-6</RefSource><PMID Version="1">21045057</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2011 Jan;39(Database issue):D561-8</RefSource><PMID Version="1">21045058</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2011 Jan;39(Database issue):D214-9</RefSource><PMID Version="1">21051339</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2011 Jan;39(Database issue):D698-704</RefSource><PMID Version="1">21071413</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2011 Jul;39(Web Server issue):W475-8</RefSource><PMID Version="1">21470960</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2011;12:420</RefSource><PMID Version="1">22032181</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2011;12 Suppl 8:S1</RefSource><PMID Version="1">22151647</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2011;12 Suppl 8:S2</RefSource><PMID Version="1">22151901</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2011;12 Suppl 8:S5</RefSource><PMID Version="1">22151999</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2012 Jan;40(Database issue):D866-75</RefSource><PMID Version="1">22096235</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nucleic Acids Res. 2012 Jan;40(Database issue):D13-25</RefSource><PMID Version="1">22140104</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2012;7(6):e38460</RefSource><PMID Version="1">22679507</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2012 Jul 1;28(13):1759-65</RefSource><PMID Version="1">22539668</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2012;13 Suppl 11:S1</RefSource><PMID Version="1">22759455</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2012;13 Suppl 11:S2</RefSource><PMID Version="1">22759456</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2012;13 Suppl 11:S4</RefSource><PMID Version="1">22759458</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2012 Aug 15;28(16):2154-61</RefSource><PMID Version="1">22711795</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2003 Mar 1;19(4):524-31</RefSource><PMID 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