The BioLexicon: a large-scale terminological resource for biomedical text mining
Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow t...
Saved in:
| Published in: | BMC Bioinformatics Vol. 12; no. 1; p. 397 |
|---|---|
| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Springer Science and Business Media LLC
12.10.2011
BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background
Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or
events
) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.
Results
This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.
Conclusions
The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. |
|---|---|
| AbstractList | Abstract Background: Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events ) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. Results: This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. Conclusions: The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. Results This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. Conclusions The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events ) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. Results This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. Conclusions The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.BACKGROUNDDue to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.RESULTSThis article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.CONCLUSIONSThe BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. |
| ArticleNumber | 397 |
| Audience | Academic |
| Author | Monica Monachini Valeria Quochi Vivian Lee John McNaught Dietrich Rebholz-Schuhmann CJ Rupp Yutaka Sasaki Sophia Ananiadou Simone Marchi Piotr Pęzik Riccardo Del Gratta Paul Thompson Giulia Venturi Nicoletta Calzolari Simonetta Montemagni |
| AuthorAffiliation | 4 Istituto di Linguistica Computazionale del CNR, Via Moruzzi 1, 56124 Pisa, Italy 3 Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK 2 National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK 1 School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK 6 Toyota Technological Institute, Nagoya, Japan 5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK |
| AuthorAffiliation_xml | – name: 3 Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK – name: 5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK – name: 1 School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK – name: 2 National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK – name: 6 Toyota Technological Institute, Nagoya, Japan – name: 4 Istituto di Linguistica Computazionale del CNR, Via Moruzzi 1, 56124 Pisa, Italy |
| Author_xml | – sequence: 1 givenname: Paul surname: Thompson fullname: Thompson, Paul email: paul.thompson@manchester.ac.uk organization: School of Computer Science, University of Manchester, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester Interdisciplinary Biocentre, University of Manchester – sequence: 2 givenname: John surname: McNaught fullname: McNaught, John organization: School of Computer Science, University of Manchester, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester Interdisciplinary Biocentre, University of Manchester – sequence: 3 givenname: Simonetta surname: Montemagni fullname: Montemagni, Simonetta organization: Istituto di Linguistica Computazionale del CNR – sequence: 4 givenname: Nicoletta surname: Calzolari fullname: Calzolari, Nicoletta organization: Istituto di Linguistica Computazionale del CNR – sequence: 5 givenname: Riccardo surname: del Gratta fullname: del Gratta, Riccardo organization: Istituto di Linguistica Computazionale del CNR – sequence: 6 givenname: Vivian surname: Lee fullname: Lee, Vivian organization: European Bioinformatics Institute, Wellcome Trust Genome Campus – sequence: 7 givenname: Simone surname: Marchi fullname: Marchi, Simone organization: Istituto di Linguistica Computazionale del CNR – sequence: 8 givenname: Monica surname: Monachini fullname: Monachini, Monica organization: Istituto di Linguistica Computazionale del CNR – sequence: 9 givenname: Piotr surname: Pezik fullname: Pezik, Piotr organization: European Bioinformatics Institute, Wellcome Trust Genome Campus – sequence: 10 givenname: Valeria surname: Quochi fullname: Quochi, Valeria organization: Istituto di Linguistica Computazionale del CNR – sequence: 11 givenname: CJ surname: Rupp fullname: Rupp, CJ organization: School of Computer Science, University of Manchester, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester Interdisciplinary Biocentre, University of Manchester – sequence: 12 givenname: Yutaka surname: Sasaki fullname: Sasaki, Yutaka organization: School of Computer Science, University of Manchester, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Toyota Technological Institute – sequence: 13 givenname: Giulia surname: Venturi fullname: Venturi, Giulia organization: Istituto di Linguistica Computazionale del CNR – sequence: 14 givenname: Dietrich surname: Rebholz-Schuhmann fullname: Rebholz-Schuhmann, Dietrich organization: European Bioinformatics Institute, Wellcome Trust Genome Campus – sequence: 15 givenname: Sophia surname: Ananiadou fullname: Ananiadou, Sophia organization: School of Computer Science, University of Manchester, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester Interdisciplinary Biocentre, University of Manchester |
| BackLink | https://cir.nii.ac.jp/crid/1872553967475733120$$DView record in CiNii https://www.ncbi.nlm.nih.gov/pubmed/21992002$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Uk1v1DAQjVAR_YA7JxQBEnBIsR07tjkglYqPlVYCQTlbjjNJXWXj1s6i8u-ZNGXpVmgVKXEm772ZvHmH2d4QBsiyp5QcU6qqt5RLWjBKREFZUWr5IDvYlPbunPezw5QuCKFSEfEo22dUa0YIO8i-nZ1D_sGHJVx7F4Z3uc17GzsokrM95CPElR9CHzqP73mEFNbRQd6GmNc-rKC5qY9wPeYI9EP3OHvY2j7Bk9vnUfbz08ez0y_F8uvnxenJsnBS6LFglkvFGk4EtIyUjAkLSjDHha6Ek21ZK2h0qRpCGVjd1qAa14AleACi6_IoW8y6TbAX5jL6lY2_TbDe3BRC7IyNo3c9mBJ0o6ECzTnhvG2UpJWuhaugbQi3ErXez1qX6xp_ycEwRttviW5_Gfy56cIvg3MrJQQKvLoViOFqDWk0K58c9L0dIKyT0UQRQpTSiHy9E0kpE4xpUVUIfX4PeoHmD2gq6klKeMmmzi9mUIfrMn5oAw7oJk1zwiRnHCecpI7_g8KrgdW0dmg91rcIb7YIiJl23Nl1Smbx4_s29tld9za2_Q0ZAsgMcDGkFKHdQCgxU47NFFQzBdVQZjDHSKnuUZwf7ejD5L_vdxHpTEzYY-gg_jNtB-flzBm8xz7TnSrJcK-6klwKWZYUM_oHJXEMDw |
| CitedBy_id | crossref_primary_10_1093_bib_bbt006 crossref_primary_10_1038_s41746_022_00730_6 crossref_primary_10_1145_2528272_2528277 crossref_primary_10_1186_2041_1480_3_3 crossref_primary_10_1093_bib_bbs018 crossref_primary_10_1186_1471_2105_14_281 crossref_primary_10_1016_j_jbi_2013_01_001 crossref_primary_10_1186_s13326_018_0193_x crossref_primary_10_1016_j_jbi_2015_05_010 crossref_primary_10_1016_j_drudis_2013_10_024 crossref_primary_10_1136_amiajnl_2011_000744 crossref_primary_10_1007_s11704_020_8426_4 crossref_primary_10_1016_j_procs_2023_10_102 crossref_primary_10_1016_j_jbi_2012_10_007 crossref_primary_10_1007_s40264_013_0064_4 crossref_primary_10_1186_1471_2105_15_S14_S5 crossref_primary_10_1371_journal_pone_0075185 crossref_primary_10_1016_j_heliyon_2024_e36351 crossref_primary_10_1016_j_jbi_2012_12_001 crossref_primary_10_1371_journal_pone_0175277 crossref_primary_10_1371_journal_pcbi_1003799 crossref_primary_10_1515_hsz_2021_0125 crossref_primary_10_3390_genes10020159 crossref_primary_10_1007_s10579_016_9344_9 crossref_primary_10_1016_j_ins_2016_04_039 crossref_primary_10_1177_1460458221989392 crossref_primary_10_1186_s13326_022_00281_5 |
| Cites_doi | 10.1142/S0219720010004586 10.1093/nar/gkp886 10.1186/1471-2105-10-326 10.1093/nar/gkp846 10.1007/11573036_36 10.1038/416373a 10.1038/nbt1346 10.1093/bioinformatics/btg105 10.1093/bioinformatics/btn469 10.1109/BICTA.2010.5645108 10.1016/j.tibtech.2006.10.002 10.1186/1471-2105-7-S3-S2 10.1002/cfg.451 10.1186/1471-2105-11-492 10.1016/j.tibtech.2010.04.005 10.1093/bioinformatics/btl155 10.1186/1471-2105-8-325 10.1186/1471-2105-9-S11-S5 10.1093/nar/gkp914 10.1002/0471250953.bi1409s26 10.1186/1471-2105-7-S3-S5 10.1038/nrg1768 10.1002/cfg.432 10.1093/bioinformatics/btm393 10.1186/1471-2105-5-155 10.1186/gb-2005-6-2-r21 10.1186/gb-2008-9-s2-s4 10.1371/journal.pone.0004554 10.1093/bioinformatics/bti749 10.1186/gb-2005-6-5-r44 10.1007/978-3-642-04235-5_28 10.1093/bioinformatics/bth227 10.7551/mitpress/7287.001.0001 10.1093/bib/bbn016 10.1093/nar/gkh061 10.1186/1471-2105-10-349 10.1186/1471-2105-9-S3-S2 10.1186/1471-2105-7-356 10.1093/bioinformatics/btq180 10.1162/0891201053630264 10.1186/1758-2946-2-3 10.3115/1609049.1609065 10.1371/journal.pone.0003158 10.1142/S0219720010004513 10.1038/75556 10.1093/nar/gkn741 10.1186/1471-2105-6-S1-S1 10.3115/1567594.1567610 |
| ContentType | Journal Article |
| Copyright | Thompson et al; licensee BioMed Central Ltd. 2011 COPYRIGHT 2011 BioMed Central Ltd. 2011 Thompson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2011 Thompson et al; licensee BioMed Central Ltd. 2011 Thompson et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Thompson et al; licensee BioMed Central Ltd. 2011 – notice: COPYRIGHT 2011 BioMed Central Ltd. – notice: 2011 Thompson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2011 Thompson et al; licensee BioMed Central Ltd. 2011 Thompson et al; licensee BioMed Central Ltd. |
| DBID | RYH C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1186/1471-2105-12-397 |
| DatabaseName | CiNii Complete Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology collection Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE Engineering Research Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 397 |
| ExternalDocumentID | oai_doaj_org_article_3e9d9e6e944044fd87169b5c6efd04a7 PMC3228855 2524260341 A274242286 21992002 10_1186_1471_2105_12_397 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Chief Scientist Office – fundername: Department of Health – fundername: Cancer Research UK – fundername: Wellcome Trust – fundername: Medical Research Council – fundername: British Heart Foundation – fundername: Arthritis Research UK – fundername: Biotechnology and Biological Sciences Research Council grantid: BB/G013160/1 |
| GroupedDBID | --- 0R~ 23N 2VQ 2WC 4.4 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C1A C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAO ICD IHR INH INR IPNFZ ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO RBZ RIG RNS ROL RPM RSV RYH SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB PUEGO AAYXX CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF M0N NPM 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c759t-2a4782d405ef203225ae852c45965c7f3b8ed938d012ea9fbe8dcdea0be8e09b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 38 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000297641800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Fri Oct 03 12:53:43 EDT 2025 Tue Nov 04 01:39:29 EST 2025 Fri Sep 05 07:11:26 EDT 2025 Mon Oct 06 18:06:52 EDT 2025 Mon Oct 06 18:28:54 EDT 2025 Tue Nov 11 10:10:23 EST 2025 Tue Nov 04 17:25:01 EST 2025 Thu Nov 13 14:15:52 EST 2025 Wed Feb 19 02:23:55 EST 2025 Sat Nov 29 05:39:53 EST 2025 Tue Nov 18 22:34:33 EST 2025 Sat Sep 06 07:27:16 EDT 2025 Mon Nov 10 09:21:05 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Biomedical Domain Text Mining Biomedical Text Semantic Role Name Entity Recognition |
| Language | English |
| License | http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c759t-2a4782d405ef203225ae852c45965c7f3b8ed938d012ea9fbe8dcdea0be8e09b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
| ORCID | 0000-0001-5849-0979 0000-0002-2953-8619 0000-0002-1321-5444 0000-0003-1867-8445 0000-0003-3356-3988 0000-0002-0994-6308 0000-0003-4320-6466 |
| OpenAccessLink | https://doaj.org/article/3e9d9e6e944044fd87169b5c6efd04a7 |
| PMID | 21992002 |
| PQID | 907104325 |
| PQPubID | 44065 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3e9d9e6e944044fd87169b5c6efd04a7 pubmedcentral_primary_oai_pubmedcentral_nih_gov_3228855 proquest_miscellaneous_908000889 proquest_miscellaneous_1125229566 proquest_journals_907104325 gale_infotracmisc_A274242286 gale_infotracacademiconefile_A274242286 gale_incontextgauss_ISR_A274242286 pubmed_primary_21992002 crossref_primary_10_1186_1471_2105_12_397 crossref_citationtrail_10_1186_1471_2105_12_397 springer_journals_10_1186_1471_2105_12_397 nii_cinii_1872553967475733120 |
| PublicationCentury | 2000 |
| PublicationDate | 2011-10-12 |
| PublicationDateYYYYMMDD | 2011-10-12 |
| PublicationDate_xml | – month: 10 year: 2011 text: 2011-10-12 day: 12 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC Bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2011 |
| Publisher | Springer Science and Business Media LLC BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: Springer Science and Business Media LLC – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | A Burgun (4943_CR27) 2008 K Kipper-Schuler (4943_CR38) 2005 4943_CR79 L Jensen (4943_CR1) 2006; 7 C Nobata (4943_CR87) 2009 J McNaught (4943_CR86) 2006 KB Cohen (4943_CR43) 2006; 7 WJ Wilbur (4943_CR73) 2006; 7 A Dolbey (4943_CR17) 2006 V Quochi (4943_CR76) 2008 L Hirschman (4943_CR60) 2006 4943_CR70 KB Cohen (4943_CR35) 2008; 3 Y Tateisi (4943_CR69) 2005 B Smith (4943_CR25) 2007; 25 Y Tsuruoka (4943_CR57) 2007; 23 A Morgan (4943_CR65) 2007 K Eilbeck (4943_CR24) 2005; 6 O Bodenreider (4943_CR29) 2004; 32 4943_CR8 4943_CR7 M Palmer (4943_CR37) 2005; 31 T Wattarujeekrit (4943_CR16) 2004; 5 4943_CR5 J-D Kim (4943_CR61) 2004 Y Tsuruoka (4943_CR64) 2008; 9 TC Wiegers (4943_CR13) 2009; 10 P Thompson (4943_CR72) 2009; 10 E Beisswanger (4943_CR23) 2008; 136 MV Blagosklonny (4943_CR2) 2002; 416 SE Wright (4943_CR78) 2004 AC Browne (4943_CR18) 2003 H Fang (4943_CR66) 2006 S Ananiadou (4943_CR3) 2006; 24 ISO-12620 (4943_CR77) 2006 T Hara (4943_CR68) 2005 M Ashburner (4943_CR6) 2000; 25 M Miwa (4943_CR32) 2010; 8 J Ruppenhofer (4943_CR39) 2006 W Hersh (4943_CR85) 2007 M He (4943_CR20) 2009; 4 V Quochi (4943_CR75) 2009 RT Tsai (4943_CR42) 2007; 8 XML (4943_CR22) 2004 E Wingender (4943_CR55) 2008; 9 L Smith (4943_CR19) 2004; 20 S Gama-Castro (4943_CR51) 2008; 36 Y Miyao (4943_CR67) 2004 4943_CR59 L Yang (4943_CR62) 2010 4943_CR12 4943_CR10 4943_CR54 4943_CR53 K Verspoor (4943_CR26) 2005; 6 J Klekota (4943_CR28) 2006; 22 4943_CR50 G Nenadic (4943_CR14) 2003; 19 J Björne (4943_CR31) 2010; 26 Y Sasaki (4943_CR81) 2009 P Thompson (4943_CR71) 2008 Y Tsuruoka (4943_CR21) 2008; 24 S Montemagni (4943_CR74) 2007 KB Cohen (4943_CR84) 2010; 11 L Hirschman (4943_CR63) 2005; 6 M Krallinger (4943_CR11) 2008; 9 4943_CR49 S Yamamoto (4943_CR56) 2004; 5 4943_CR46 S Ananiadou (4943_CR30) 2010; 28 4943_CR45 UniProt Consortium (4943_CR15) 2010; 38 4943_CR44 J Bard (4943_CR48) 2005; 6 A Ruepp (4943_CR52) 2010; 38 W Frawley (4943_CR34) 1992 Y Sasaki (4943_CR58) 2008; 9 4943_CR83 S Ananiadou (4943_CR33) 2006 4943_CR82 4943_CR80 H Liu (4943_CR9) 2006; 22 S Pyysalo (4943_CR40) 2006; 7 G Venturi (4943_CR36) 2009 Y Sasaki (4943_CR4) 2010; 8 P Pezik (4943_CR47) 2008 KW Fung (4943_CR41) 2007; 129 |
| References_xml | – volume: 8 start-page: 131 issue: 1 year: 2010 ident: 4943_CR32 publication-title: J Bioinform Comput Biol doi: 10.1142/S0219720010004586 – ident: 4943_CR8 doi: 10.1093/nar/gkp886 – volume: 10 start-page: 326 year: 2009 ident: 4943_CR13 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-326 – volume: 38 start-page: D142 issue: Database issue year: 2010 ident: 4943_CR15 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkp846 – ident: 4943_CR70 doi: 10.1007/11573036_36 – volume: 416 start-page: 373 issue: 6879 year: 2002 ident: 4943_CR2 publication-title: Nature doi: 10.1038/416373a – volume: 25 start-page: 1251 issue: 11 year: 2007 ident: 4943_CR25 publication-title: Nat Biotechnol doi: 10.1038/nbt1346 – volume: 19 start-page: 938 issue: 8 year: 2003 ident: 4943_CR14 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg105 – ident: 4943_CR45 – start-page: 34 volume-title: Proceedings of the International Conferences on Digital Libraries and the Semantic Web year: 2009 ident: 4943_CR87 – ident: 4943_CR80 – ident: 4943_CR49 – volume: 24 start-page: 2559 issue: 21 year: 2008 ident: 4943_CR21 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn469 – volume-title: FrameNet II: Extended Theory and Practice year: 2006 ident: 4943_CR39 – start-page: 1061 volume-title: Proceedings of the 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications year: 2010 ident: 4943_CR62 doi: 10.1109/BICTA.2010.5645108 – volume: 24 start-page: 571 issue: 12 year: 2006 ident: 4943_CR3 publication-title: Trends Biotechnol doi: 10.1016/j.tibtech.2006.10.002 – start-page: 143 volume-title: Text Mining for Biology and Biomedicine year: 2006 ident: 4943_CR86 – start-page: 2159 volume-title: Proceeings of LREC year: 2008 ident: 4943_CR71 – volume: 7 start-page: S2 issue: Suppl 3 year: 2006 ident: 4943_CR40 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-S3-S2 – ident: 4943_CR5 – start-page: 222 volume-title: Proceedings of IJCNLP year: 2005 ident: 4943_CR69 – volume: 6 start-page: 61 issue: 1-2 year: 2005 ident: 4943_CR26 publication-title: Comp Funct Genomics doi: 10.1002/cfg.451 – volume: 36 start-page: D120 issue: Database issue year: 2008 ident: 4943_CR51 publication-title: Nucleic Acids Res – start-page: 199 volume-title: Proceedings of IJCNLP year: 2005 ident: 4943_CR68 – volume: 11 start-page: 492 year: 2010 ident: 4943_CR84 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-11-492 – volume: 28 start-page: 381 issue: 7 year: 2010 ident: 4943_CR30 publication-title: Trends Biotechnol doi: 10.1016/j.tibtech.2010.04.005 – start-page: 67 volume-title: Text Mining for Biology and Biomedicine year: 2006 ident: 4943_CR33 – ident: 4943_CR50 – volume: 22 start-page: 1670 issue: 13 year: 2006 ident: 4943_CR28 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl155 – volume: 8 start-page: 325 year: 2007 ident: 4943_CR42 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-325 – ident: 4943_CR44 – start-page: 35 volume-title: Proceedings of the LREC Workshop on Building and Evaluating Resources for Biomedical Text Mining year: 2008 ident: 4943_CR47 – start-page: 798 volume-title: AMIA Annu Symp Proc year: 2003 ident: 4943_CR18 – volume: 9 start-page: S5 issue: Suppl 11 year: 2008 ident: 4943_CR58 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-S11-S5 – volume: 38 start-page: D497 issue: Database issue year: 2010 ident: 4943_CR52 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkp914 – start-page: 87 volume-title: Proceedings of KR-MED 2006: Biomedical Ontology in Action year: 2006 ident: 4943_CR17 – ident: 4943_CR54 – start-page: 213 volume-title: Text Mining for Biology and Biomedicine year: 2006 ident: 4943_CR60 – start-page: 684 volume-title: Proceedings of IJCNLP year: 2004 ident: 4943_CR67 – ident: 4943_CR79 – ident: 4943_CR7 doi: 10.1002/0471250953.bi1409s26 – volume: 7 start-page: S5 issue: Suppl 3 year: 2006 ident: 4943_CR43 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-S3-S5 – volume: 7 start-page: 119 issue: 2 year: 2006 ident: 4943_CR1 publication-title: Nat Rev Genet doi: 10.1038/nrg1768 – volume: 5 start-page: 528 issue: 6-7 year: 2004 ident: 4943_CR56 publication-title: Comp Funct Genomics doi: 10.1002/cfg.432 – volume: 23 start-page: 2768 issue: 20 year: 2007 ident: 4943_CR57 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm393 – volume: 5 start-page: 155 year: 2004 ident: 4943_CR16 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-5-155 – volume: 6 start-page: R21 issue: 2 year: 2005 ident: 4943_CR48 publication-title: Genome biology doi: 10.1186/gb-2005-6-2-r21 – start-page: 123 volume-title: Proceedings of LREC year: 2004 ident: 4943_CR78 – volume: 9 start-page: S4 issue: Suppl 2 year: 2008 ident: 4943_CR11 publication-title: Genome Biol doi: 10.1186/gb-2008-9-s2-s4 – volume: 4 start-page: e4554 issue: 2 year: 2009 ident: 4943_CR20 publication-title: PLoS ONE doi: 10.1371/journal.pone.0004554 – volume: 22 start-page: 103 issue: 1 year: 2006 ident: 4943_CR9 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti749 – volume: 6 start-page: R44 issue: 5 year: 2005 ident: 4943_CR24 publication-title: Genome Biol doi: 10.1186/gb-2005-6-5-r44 – start-page: 325 volume-title: Human Language Technology Challenges of the Information Society: Third Language and Technology Conference (LTC) year: 2009 ident: 4943_CR75 doi: 10.1007/978-3-642-04235-5_28 – ident: 4943_CR82 – start-page: 2285 volume-title: Proceedings of LREC year: 2008 ident: 4943_CR76 – volume: 20 start-page: 2320 issue: 14 year: 2004 ident: 4943_CR19 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth227 – ident: 4943_CR59 doi: 10.7551/mitpress/7287.001.0001 – volume-title: Linguistic semantics year: 1992 ident: 4943_CR34 – volume-title: PhD thesis year: 2005 ident: 4943_CR38 – volume: 9 start-page: 326 issue: 4 year: 2008 ident: 4943_CR55 publication-title: Brief Bioinform doi: 10.1093/bib/bbn016 – volume-title: Extensible Markup Language year: 2004 ident: 4943_CR22 – ident: 4943_CR53 – volume-title: Proceedings of TREC year: 2007 ident: 4943_CR85 – volume: 32 start-page: D267 issue: Database issue year: 2004 ident: 4943_CR29 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkh061 – volume-title: Terminology and other content language resources - Data Categories - Specifications of data categories and management of a Data Category Registry for language resources year: 2006 ident: 4943_CR77 – volume: 10 start-page: 349 year: 2009 ident: 4943_CR72 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-349 – volume: 9 start-page: S2 issue: Suppl 3 year: 2008 ident: 4943_CR64 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-S3-S2 – volume: 7 start-page: 356 year: 2006 ident: 4943_CR73 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-356 – volume: 26 start-page: i382 issue: 12 year: 2010 ident: 4943_CR31 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq180 – start-page: 41 volume-title: Proceedings of the BioNLP Workshop year: 2006 ident: 4943_CR66 – volume: 31 start-page: 71 issue: 1 year: 2005 ident: 4943_CR37 publication-title: Computational Linguistics doi: 10.1162/0891201053630264 – volume: 129 start-page: 605 issue: Pt 1 year: 2007 ident: 4943_CR41 publication-title: Stud Health Technol Inform – ident: 4943_CR12 doi: 10.1186/1758-2946-2-3 – start-page: 7 volume-title: Proceedings of the Second BioCreative Challenge Evaluation Workshop year: 2007 ident: 4943_CR65 – start-page: 61 volume-title: Proceedings of EACL: Demonstrations Session year: 2009 ident: 4943_CR81 doi: 10.3115/1609049.1609065 – volume: 3 start-page: e3158 issue: 9 year: 2008 ident: 4943_CR35 publication-title: PLoS ONE doi: 10.1371/journal.pone.0003158 – ident: 4943_CR46 – volume-title: Event annotation of domain corpora, BOOTStrep (FP6 - 028099), Deliverable 4.1 year: 2007 ident: 4943_CR74 – ident: 4943_CR83 – start-page: 91 volume-title: Yearb Med Inform year: 2008 ident: 4943_CR27 – volume: 8 start-page: 147 issue: 1 year: 2010 ident: 4943_CR4 publication-title: J Bioinform Comput Biol doi: 10.1142/S0219720010004513 – volume: 25 start-page: 25 issue: 1 year: 2000 ident: 4943_CR6 publication-title: Nat Genet doi: 10.1038/75556 – ident: 4943_CR10 doi: 10.1093/nar/gkn741 – volume: 6 start-page: S1 issue: Suppl 1 year: 2005 ident: 4943_CR63 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-S1-S1 – start-page: 137 volume-title: Proceedings of CICLING year: 2009 ident: 4943_CR36 – start-page: 70 volume-title: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA) year: 2004 ident: 4943_CR61 doi: 10.3115/1567594.1567610 – volume: 136 start-page: 9 year: 2008 ident: 4943_CR23 publication-title: Stud Health Technol Inform |
| SSID | ssj0017805 ssib002804394 |
| Score | 2.2557576 |
| Snippet | Background
Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to... Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for... Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to... Abstract Background: Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help... Background: Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to... Abstract Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer nii |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 397 |
| SubjectTerms | Algorithms Biochemistry Bioinformatics Biology (General) Biomedical and Life Sciences Computational Biology Computational Biology/Bioinformatics computational lexicography Computational Lexicon Computer Appl. in Life Sciences Computer applications to medicine. Medical informatics Computer Science Applications Data Mining Databases, Factual Humans Indexing in process Information Extraction Information management lexical standards Life Sciences Medical sciences Microarrays Molecular Biology named entity recognition Networks analysis QH301-705.5 R858-859.7 Research Article Semantics terminography Terminology Text Mining Vocabulary, Controlled |
| SummonAdditionalLinks | – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZoAakX3oXQFhmEhECKNnHixOaCCqICqapWPKTeLMd2ykptUja7CP49M44TCNBeuKx2k4njzIznsZ58Q8jTHFyuKbmEyE3qOLcpj2VheGzB-WqmtUwqD-J6WB4dieNjOQ-1OV0oqxxsojfUtjX4H_lMoi_MM8ZfnX-NsWkUbq6GDhob5CqCJDBfuTcfNxEQrn_YmRTFLAU7HEOGwz1OH6I8_eaJPGD_aJY3msXiXyHn35WTf2yfeq90cPM_n-cWuRHCUbrf689tcsU1d8j1vkHlj7tkDlpE4deh-w4a07ykmp5i6XjcgWgd7StpBvNJl2ErgEIgTPv3-v1xLC6hZ74TxT3y-eDtpzfv4tCDIUYJrmKmc4ghLIR1rsZm64xrJzgzOZcFN2WdVcJZmQkLjs5pWVdOWGOdTuCLS2SVbZPNpm3cA0IzC7GRZZmFi_LCyQoGK7U1daJFWQkWkdkgD2UCQDn2yThVPlERhUIJKpSgSpkCCUbk-XjFeQ_OcQntaxTxSIew2v5AuzxRYZWqzEkrHcwNURPz2mI2KStuClfbJNcwyBNUEIXAGQ0y70Svu069__hB7eOeN-KpFRF5FojqFuZvdHjRAbiAWFsTyt0JJaxsMzm9B3oIvMDPVJSQ_WWygPwPMSxTlkRkZ1AtFSxPp0a9isjj8SwOjMV0jWvXHfCHceziXsAd6AU0EjMJLIGLyP1e5UfOMaxYBkcakXKyGCasnZ5pFl88cjnojxAcpvZiWDa_Jn6R4B5e-pQ7ZIuFqsyU7ZLN1XLt9sg182216JaPvD34CdfEYnk priority: 102 providerName: ProQuest – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3raxQxEB_aquAX34-1rUQRRGHpbnazm_itikWhlNKq9FvIJtl60O6V2zvR_96Z7ENWW0G_HHebSS43mczjMvkNwIscTa4thULPTZk4d6mIVWFF7ND4Gm6MSqoA4rpfHhzIkxN1uAZ8uAsTst2HI8mgqcO2lsVOimo0xgBFBJg9Va7DNTR2kso1HB1_GU8OCKN_OI68pNfE_ASU_lEXrzez2WV-5p_pkr-dmQZTtHf7f37EHbjVO55st5OUu7Dmm3twoytF-eM-HKK8MPy077-jbDRvmGFnlCQet7iInnU5M4OiZIv-T3-GLi_rbvCH55RGws5DzYkH8Hnv_ad3H-K-2kJMa7WMucnRW3DowPmayqpzYbwU3OZCFcKWdVZJ71QmHZo0b1Rdeems8ybBNz5RVfYQNpp54x8Dyxx6QY5nDjvlhVcVDlYaZ-vEyLKSPIKdYRG07aHIqSLGmQ4hiSw08UkTn3TKNfIpgldjj4sOhuMvtG9pXUc6AtAOD-aLU93vR5155ZTHuRE-Yl47ihtVJWzha5fkBgd5TlKhCSKjIeadmlXb6o_HR3qXTrcJOa2I4GVPVM9x_tb0VxqQC4SqNaHcmlDiHraT5m0UPuQFvaayxDgvUwVGeoRWmfIkgs1BLHWvY1qtyDvMMy4ieDa20sCUNtf4-apF_nBB9doL_AZ2BY2imIGS3SJ41Mn5yDlOucloMiMoJztgwtppSzP7GjDKUX6kFDi118M--DXxqxbuyb8Qb8JN3qdjpnwLNpaLld-G6_bbctYungad8BOpu1dO priority: 102 providerName: Springer Nature |
| Title | The BioLexicon: a large-scale terminological resource for biomedical text mining |
| URI | https://cir.nii.ac.jp/crid/1872553967475733120 https://link.springer.com/article/10.1186/1471-2105-12-397 https://www.ncbi.nlm.nih.gov/pubmed/21992002 https://www.proquest.com/docview/907104325 https://www.proquest.com/docview/1125229566 https://www.proquest.com/docview/908000889 https://pubmed.ncbi.nlm.nih.gov/PMC3228855 https://doaj.org/article/3e9d9e6e944044fd87169b5c6efd04a7 |
| Volume | 12 |
| WOSCitedRecordID | wos000297641800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: Open Access: BioMedCentral Open Access Titles customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3db9MwELfYAIkXxDdhWxUQEgIpauLEsc3bhjYxMaqoA1R4sRzbgUojRf1A8N9z53ywAIMXXqzGviTu3dV3V59_R8jjDEyu4UyC5yZ1lNmERTI3LLJgfDXVWsalB3E94ZOJmM1kca7UF-aENfDADePGqZNWutxJBLLLKosOviyZyV1l40z7c-Qxl10w1e4fIFK_P1fEkwiCGtZtUIp83Pd5uD4EezpnkDxuf786b9Xz-Z88z98TKH_ZRfXG6egGud56leF-821ukkuuvkWuNnUmv98mBShDCFcn7hsIvn4e6vAMM8CjFUjIhU1CTLcKhsv2H_0Q_NmwOZ7v-zFHJPzsC0rcIW-PDt-8eBm1pRQiFMQ6ojoDV8CCd-YqrJlOmXaCUZMxmTPDq7QUzspUWLBXTsuqdMIa63QMH1wsy_Qu2a4XtbtPwtSCi2NpauGmDORSwsO4tqaKteCloAEZd_xUpsUZx3IXZ8rHGyJXKAGFElAJVSCBgDzt7_jSYGz8hfYARdTTITq27wCdUa3OqH_pTEAeoYAV4l_UyLyPerNaqePTqdrHrWuERcsD8qQlqhYwf6Pb8wrABYTMGlDuDijhB2oGw3ugR8ALbBPBIYhLZQ5hHEJRJjQOyE6nYapdQFZKouuXpZQF5GE_ig_GnLjaLTYr4A9lWIw9hzeEF9BIDAgwky0g9xqV7TlHMfEY7GFA-ECZB6wdjtTzTx6AHPRHCAZTe9ap_c-JXyS4B_9DcDvkGm1zMBO6S7bXy43bI1fM1_V8tRyRLT7jvhUjcvngcFJMR35ZgPYVj0aY11tAW7APMF4cvy7ew9X09N0PyMdgNA |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLa2AYIX7pewDQwCIZCiJk6c2EgIjcu0qmWa2JD2ZhzbGZVGOpoW2I_iP3JObhBge9sDL1UbnzjOybnWJ98h5FEMLtekXELkJrUf25D7MjHct-B8NdNaBlkF4jpOt7fF_r7cWSI_2ndhsKyytYmVobZTg_-RDyT6wjhi_OXRFx-bRuHmattBo5aKkTv-Bhlb-WL4Bh7vY8Y23-693vKbpgI-LmnuMx2DU7QQp7gcu4czrp3gzMRcJtykeZQJZ2UkLFhup2WeOWGNdTqALy6QWQTzLpNzcSRSVKtR6nebFtgeoN0JFckgBLvvQ0bFK1xARJX6zfNVDQI6N7BcTCb_CnH_rtT8Y7u28oKbV_4z_l0ll5twm27U-nGNLLniOrlQN-A8vkF2QEso_Bq776ARxXOq6SGWxvsliK6jdaVQ6x7orNnqoBDo0xq3oDqOxTP0c9Vp4yb5cCa3c4usFNPC3SE0shD7WRZZOClOnMxgslRbkwdapJlgHhm0z1-ZBoAd-4AcqioRE4lCiVEoMSpkCiTGI0-7M45q8JFTaF-hSHV0CBteHZjODlRjhVTkpJUO1oaokHFuMVuWGTeJy20Qa5jkIQqkQmCQApl3oBdlqYa779UG7ukjXlzikScNUT6F9RvdvMgBXEAssR7lWo8SLJfpDa-D3AMv8DMUKWS3kUwgv0WMzpAFHlltRVk1lrVUnRx75EE3ihNjsWDhposS-MM4dqlP4Ar0BBqJmRKW-Hnkdq1iHecYVmRDoOCRtKd8Pdb2R4rJpwqZHeRHCA5Le9aq6a-Fn_Tg7p56l_fJxa29d2M1Hm6PVskl1lSghmyNrMxnC7dOzpuv80k5u1fZIko-nrXy_gTFx8Cq |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3rb9MwED-x8RBfeD_CNjAICYEUNXXixObbeFRMVFXFAO2b5djOqDTSqWkR_Pfc5YUCGxLiS9XGZ9c9n313vfPvAJ4mqHJtJhRabsqEiRuLUKVWhA6Vr-HGqCivQVyn2Wwmj47UvP3Dreqy3buQZHOngVCayvXo1BXNFpfpaIxHaojOiqgh91S2BRcTSqMnb_3wcx9FILz-LjR5Rq-BKqoR-_tzeatcLM6yOf9Mnfwtflqrpcn1__1BN-Baa5Cy_UaCbsIFX96Cy02Jyh-3YY5yxPDT1H9HmSlfMsNOKHk8rHBxPWtyaboDlK3aYABDU5g1N_vr55Rewr7WtSjuwKfJ24-v34VtFYaQ1nAdcpOgFeHQsPMFlVvnwngpuE2ESoXNijiX3qlYOlR13qgi99JZ502Eb3yk8vgubJfL0t8HFju0jhyPHXZKUq9yHCwzzhaRkVkueQCjbkG0bSHKqVLGia5dFZlq4pMmPukx18inAJ73PU4beI6_0L6iNe7pCFi7frBcHet2n-rYK6c8zo1wE5PCkT-pcmFTX7goMTjIE5IQTdAZJTHv2GyqSh8cftD7FPUmRLU0gGctUbHE-VvTXnVALhDa1oByd0CJe9sOmvdQEJEX9DqWGfp_sUrRAyQUyzGPAtjpRFS3Z0-lFVmNScxFAI_7VhqY0ulKv9xUyB8uqI57it_AzqFR5EtQElwA9xqZ7znHKWcZVWkA2WA3DFg7bCkXX2rscpQfKQVO7UW3J35N_LyFe_AvxI_gyvzNRE8PZu934CpvMzbHfBe216uN34NL9tt6Ua0e1kfFTx-fYxY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+BioLexicon%3A+a+large-scale+terminological+resource+for+biomedical+text+mining&rft.jtitle=BMC+bioinformatics&rft.au=Thompson%2C+Paul&rft.au=McNaught%2C+John&rft.au=Montemagni%2C+Simonetta&rft.au=Calzolari%2C+Nicoletta&rft.date=2011-10-12&rft.pub=BioMed+Central&rft.eissn=1471-2105&rft.volume=12&rft.spage=397&rft.epage=397&rft_id=info:doi/10.1186%2F1471-2105-12-397&rft_id=info%3Apmid%2F21992002&rft.externalDocID=PMC3228855 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |