Concept recognition for extracting protein interaction relations from biomedical text View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-09-01

AUTHORS

William A Baumgartner, Zhiyong Lu, Helen L Johnson, J Gregory Caporaso, Jesse Paquette, Anna Lindemann, Elizabeth K White, Olga Medvedeva, K Bretonnel Cohen, Lawrence Hunter

ABSTRACT

BACKGROUND: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. RESULTS: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. CONCLUSION: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net. More... »

PAGES

s9-s9

References to SciGraph publications

  • 2008-09-01. Overview of the protein-protein interaction annotation extraction task of BioCreative II in GENOME BIOLOGY
  • 2007-09-13. Corpus Refactoring: a Feasibility Study in JOURNAL OF BIOMEDICAL DISCOVERY AND COLLABORATION
  • 2000-05. Gene Ontology: tool for the unification of biology in NATURE GENETICS
  • 2005-05-24. Evaluation of BioCreAtIvE assessment of task 2 in BMC BIOINFORMATICS
  • 2008-01-31. OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression in BMC BIOINFORMATICS
  • 2008-06-27. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function in GENOME BIOLOGY
  • 2005-05-24. Overview of BioCreAtIvE task 1B: normalized gene lists in BMC BIOINFORMATICS
  • 2005-05-24. BioCreAtIvE Task1A: entity identification with a stochastic tagger in BMC BIOINFORMATICS
  • 2008-09-01. MINT and IntAct contribute to the Second BioCreative challenge: serving the text-mining community with high quality molecular interaction data in GENOME BIOLOGY
  • 2008-06-27. Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy in GENOME BIOLOGY
  • 2008-09-01. Overview of BioCreative II gene normalization in GENOME BIOLOGY
  • 2008-09-01. Overview of BioCreative II gene mention recognition in GENOME BIOLOGY
  • 2008-06-27. Predicting gene function in a hierarchical context with an ensemble of classifiers in GENOME BIOLOGY
  • 2005-05-24. BioCreAtIvE Task 1A: gene mention finding evaluation in BMC BIOINFORMATICS
  • 2008-06-27. A critical assessment of Mus musculus gene function prediction using integrated genomic evidence in GENOME BIOLOGY
  • 2006-03-13. Enhancing access to the Bibliome: the TREC 2004 Genomics Track in JOURNAL OF BIOMEDICAL DISCOVERY AND COLLABORATION
  • 2005. Developing a Robust Part-of-Speech Tagger for Biomedical Text in ADVANCES IN INFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/gb-2008-9-s2-s9

    DOI

    http://dx.doi.org/10.1186/gb-2008-9-s2-s9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1047888375

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/18834500


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information Systems", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Biomedical Research", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Databases, Bibliographic", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genes", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Information Storage and Retrieval", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Pattern Recognition, Automated", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Protein Interaction Mapping", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Baumgartner", 
            "givenName": "William A", 
            "id": "sg:person.0601522464.08", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601522464.08"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lu", 
            "givenName": "Zhiyong", 
            "id": "sg:person.01074355715.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074355715.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Johnson", 
            "givenName": "Helen L", 
            "id": "sg:person.01250122671.15", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01250122671.15"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Caporaso", 
            "givenName": "J Gregory", 
            "id": "sg:person.0624224157.70", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624224157.70"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Paquette", 
            "givenName": "Jesse", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lindemann", 
            "givenName": "Anna", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "White", 
            "givenName": "Elizabeth K", 
            "id": "sg:person.01170711253.75", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01170711253.75"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Medvedeva", 
            "givenName": "Olga", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cohen", 
            "givenName": "K Bretonnel", 
            "id": "sg:person.01155023050.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01155023050.51"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA", 
              "id": "http://www.grid.ac/institutes/grid.266190.a", 
              "name": [
                "Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hunter", 
            "givenName": "Lawrence", 
            "id": "sg:person.013347016417.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013347016417.86"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/1747-5333-2-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012341552", 
              "https://doi.org/10.1186/1747-5333-2-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11573036_36", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048946343", 
              "https://doi.org/10.1007/11573036_36"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-s1-s11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050375602", 
              "https://doi.org/10.1186/1471-2105-6-s1-s11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s2-s4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010159966", 
              "https://doi.org/10.1186/gb-2008-9-s2-s4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-s1-s16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038229739", 
              "https://doi.org/10.1186/1471-2105-6-s1-s16"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s2-s5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022366302", 
              "https://doi.org/10.1186/gb-2008-9-s2-s5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-s1-s2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048957855", 
              "https://doi.org/10.1186/1471-2105-6-s1-s2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s2-s2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041411233", 
              "https://doi.org/10.1186/gb-2008-9-s2-s2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s1-s2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039025062", 
              "https://doi.org/10.1186/gb-2008-9-s1-s2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1747-5333-1-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046185092", 
              "https://doi.org/10.1186/1747-5333-1-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-9-78", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030172418", 
              "https://doi.org/10.1186/1471-2105-9-78"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s1-s3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006959314", 
              "https://doi.org/10.1186/gb-2008-9-s1-s3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s1-s5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010873868", 
              "https://doi.org/10.1186/gb-2008-9-s1-s5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-s1-s4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019731137", 
              "https://doi.org/10.1186/1471-2105-6-s1-s4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s1-s4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024435781", 
              "https://doi.org/10.1186/gb-2008-9-s1-s4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-s2-s3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004073806", 
              "https://doi.org/10.1186/gb-2008-9-s2-s3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/75556", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044135237", 
              "https://doi.org/10.1038/75556"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2008-09-01", 
        "datePublishedReg": "2008-09-01", 
        "description": "BACKGROUND: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing.\nRESULTS: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist.\nCONCLUSION: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/gb-2008-9-s2-s9", 
        "inLanguage": "en", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2545550", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2545518", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1023439", 
            "issn": [
              "1474-760X", 
              "1465-6906"
            ], 
            "name": "Genome Biology", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "Suppl 2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "9"
          }
        ], 
        "keywords": [
          "concept recognition", 
          "biomedical text", 
          "use cases", 
          "biomedical text mining community", 
          "current information extraction technology", 
          "information extraction technology", 
          "biomedical language processing", 
          "information extraction applications", 
          "text mining community", 
          "relation extraction system", 
          "concept semantics", 
          "language processing", 
          "mining community", 
          "extraction applications", 
          "biomedical literature", 
          "knowledge resources", 
          "extraction efforts", 
          "difficult task", 
          "semantics", 
          "interaction relations", 
          "high quality data", 
          "extraction system", 
          "recognition", 
          "task", 
          "key information", 
          "extraction technology", 
          "modular construction", 
          "integrated approach", 
          "quality data", 
          "text", 
          "Internet", 
          "system", 
          "biologists", 
          "goal", 
          "technology", 
          "processing", 
          "information", 
          "resources", 
          "tool", 
          "applications", 
          "effectiveness", 
          "project", 
          "issues", 
          "potential uses", 
          "link", 
          "standards", 
          "knowledge", 
          "construction", 
          "efforts", 
          "performance standards", 
          "direct link", 
          "valuable tool", 
          "data", 
          "opportunities", 
          "community", 
          "field", 
          "part", 
          "special issue", 
          "unique opportunity", 
          "cases", 
          "literature", 
          "uses", 
          "relation", 
          "approach", 
          "Reliable information extraction applications", 
          "previous biomedical information extraction efforts", 
          "biomedical information extraction efforts", 
          "information extraction efforts", 
          "BioCreative II tasks", 
          "II tasks", 
          "protein interaction relation extraction system", 
          "interaction relation extraction system", 
          "benchside biologist", 
          "BioCreative Meta-Server project", 
          "Meta-Server project", 
          "protein interaction relations"
        ], 
        "name": "Concept recognition for extracting protein interaction relations from biomedical text", 
        "pagination": "s9-s9", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1047888375"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/gb-2008-9-s2-s9"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "18834500"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/gb-2008-9-s2-s9", 
          "https://app.dimensions.ai/details/publication/pub.1047888375"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2021-11-01T18:11", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/article/article_471.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/gb-2008-9-s2-s9"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/gb-2008-9-s2-s9'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/gb-2008-9-s2-s9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/gb-2008-9-s2-s9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/gb-2008-9-s2-s9'


     

    This table displays all metadata directly associated to this object as RDF triples.

    298 TRIPLES      22 PREDICATES      126 URIs      100 LITERALS      13 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/gb-2008-9-s2-s9 schema:about N1fea8c4299504613a17939ba6c134b85
    2 N49e25a2c62194ec4aa6fcd12cef58c55
    3 N651b06b3d5404eed9a7ac1613231013f
    4 N8737e0fca2c6411888cad6bd3408e55a
    5 Ndc3f8259a54f499f9b03cf7df3af1d68
    6 Ne17e60a2ab814ae0b7de2a48e469596b
    7 anzsrc-for:08
    8 anzsrc-for:0801
    9 anzsrc-for:0806
    10 schema:author N82dcd158da214f30bfbeca630b2f8390
    11 schema:citation sg:pub.10.1007/11573036_36
    12 sg:pub.10.1038/75556
    13 sg:pub.10.1186/1471-2105-6-s1-s11
    14 sg:pub.10.1186/1471-2105-6-s1-s16
    15 sg:pub.10.1186/1471-2105-6-s1-s2
    16 sg:pub.10.1186/1471-2105-6-s1-s4
    17 sg:pub.10.1186/1471-2105-9-78
    18 sg:pub.10.1186/1747-5333-1-3
    19 sg:pub.10.1186/1747-5333-2-4
    20 sg:pub.10.1186/gb-2008-9-s1-s2
    21 sg:pub.10.1186/gb-2008-9-s1-s3
    22 sg:pub.10.1186/gb-2008-9-s1-s4
    23 sg:pub.10.1186/gb-2008-9-s1-s5
    24 sg:pub.10.1186/gb-2008-9-s2-s2
    25 sg:pub.10.1186/gb-2008-9-s2-s3
    26 sg:pub.10.1186/gb-2008-9-s2-s4
    27 sg:pub.10.1186/gb-2008-9-s2-s5
    28 schema:datePublished 2008-09-01
    29 schema:datePublishedReg 2008-09-01
    30 schema:description BACKGROUND: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. RESULTS: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. CONCLUSION: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net.
    31 schema:genre article
    32 schema:inLanguage en
    33 schema:isAccessibleForFree true
    34 schema:isPartOf N3f0c9b81ccc34c499a454c686435fdb8
    35 N5373f926661e4340a2165021ba883e34
    36 sg:journal.1023439
    37 schema:keywords BioCreative II tasks
    38 BioCreative Meta-Server project
    39 II tasks
    40 Internet
    41 Meta-Server project
    42 Reliable information extraction applications
    43 applications
    44 approach
    45 benchside biologist
    46 biologists
    47 biomedical information extraction efforts
    48 biomedical language processing
    49 biomedical literature
    50 biomedical text
    51 biomedical text mining community
    52 cases
    53 community
    54 concept recognition
    55 concept semantics
    56 construction
    57 current information extraction technology
    58 data
    59 difficult task
    60 direct link
    61 effectiveness
    62 efforts
    63 extraction applications
    64 extraction efforts
    65 extraction system
    66 extraction technology
    67 field
    68 goal
    69 high quality data
    70 information
    71 information extraction applications
    72 information extraction efforts
    73 information extraction technology
    74 integrated approach
    75 interaction relation extraction system
    76 interaction relations
    77 issues
    78 key information
    79 knowledge
    80 knowledge resources
    81 language processing
    82 link
    83 literature
    84 mining community
    85 modular construction
    86 opportunities
    87 part
    88 performance standards
    89 potential uses
    90 previous biomedical information extraction efforts
    91 processing
    92 project
    93 protein interaction relation extraction system
    94 protein interaction relations
    95 quality data
    96 recognition
    97 relation
    98 relation extraction system
    99 resources
    100 semantics
    101 special issue
    102 standards
    103 system
    104 task
    105 technology
    106 text
    107 text mining community
    108 tool
    109 unique opportunity
    110 use cases
    111 uses
    112 valuable tool
    113 schema:name Concept recognition for extracting protein interaction relations from biomedical text
    114 schema:pagination s9-s9
    115 schema:productId N793b2a2d44474526ad5b3d58ba7b79e9
    116 Nb88ef2610f02464d87572dc3465d6845
    117 Ne810a6c6b2ac4496acdc7656b7956a00
    118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047888375
    119 https://doi.org/10.1186/gb-2008-9-s2-s9
    120 schema:sdDatePublished 2021-11-01T18:11
    121 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    122 schema:sdPublisher N61c5ca932af741418aa3735eeaf343d2
    123 schema:url https://doi.org/10.1186/gb-2008-9-s2-s9
    124 sgo:license sg:explorer/license/
    125 sgo:sdDataset articles
    126 rdf:type schema:ScholarlyArticle
    127 N1fea8c4299504613a17939ba6c134b85 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    128 schema:name Information Storage and Retrieval
    129 rdf:type schema:DefinedTerm
    130 N35b31cfb6a9c4b239106ec0f1d225e72 rdf:first sg:person.01074355715.83
    131 rdf:rest N84d65719ad6b4bdb9a8c782e73e44aaf
    132 N3f0c9b81ccc34c499a454c686435fdb8 schema:issueNumber Suppl 2
    133 rdf:type schema:PublicationIssue
    134 N49e25a2c62194ec4aa6fcd12cef58c55 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    135 schema:name Databases, Bibliographic
    136 rdf:type schema:DefinedTerm
    137 N4ef3fa4a9b9548ff9d4c2a0a639ecd50 rdf:first sg:person.01170711253.75
    138 rdf:rest Na1edc0355c32447c8ebfc0d5a924fc3b
    139 N5373f926661e4340a2165021ba883e34 schema:volumeNumber 9
    140 rdf:type schema:PublicationVolume
    141 N61c5ca932af741418aa3735eeaf343d2 schema:name Springer Nature - SN SciGraph project
    142 rdf:type schema:Organization
    143 N651b06b3d5404eed9a7ac1613231013f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    144 schema:name Genes
    145 rdf:type schema:DefinedTerm
    146 N6cc9524faba34dd68342c86090dd8b64 schema:affiliation grid-institutes:grid.266190.a
    147 schema:familyName Paquette
    148 schema:givenName Jesse
    149 rdf:type schema:Person
    150 N793b2a2d44474526ad5b3d58ba7b79e9 schema:name pubmed_id
    151 schema:value 18834500
    152 rdf:type schema:PropertyValue
    153 N8220d43a4ae040f681d7433a4ee09516 schema:affiliation grid-institutes:grid.266190.a
    154 schema:familyName Medvedeva
    155 schema:givenName Olga
    156 rdf:type schema:Person
    157 N82dcd158da214f30bfbeca630b2f8390 rdf:first sg:person.0601522464.08
    158 rdf:rest N35b31cfb6a9c4b239106ec0f1d225e72
    159 N84d65719ad6b4bdb9a8c782e73e44aaf rdf:first sg:person.01250122671.15
    160 rdf:rest N90e54f0343164a34bc9f5b7ed1068905
    161 N8737e0fca2c6411888cad6bd3408e55a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    162 schema:name Pattern Recognition, Automated
    163 rdf:type schema:DefinedTerm
    164 N89865f70ea3642f0a7768e07cfd83dd3 rdf:first sg:person.01155023050.51
    165 rdf:rest Naffac98083ec4ce482817726f2ea8bb2
    166 N90e54f0343164a34bc9f5b7ed1068905 rdf:first sg:person.0624224157.70
    167 rdf:rest N98373007bbb74c54a99ce552012f2cb5
    168 N98373007bbb74c54a99ce552012f2cb5 rdf:first N6cc9524faba34dd68342c86090dd8b64
    169 rdf:rest Nd109b4b696c14ea48844819390cc1c8c
    170 Na1edc0355c32447c8ebfc0d5a924fc3b rdf:first N8220d43a4ae040f681d7433a4ee09516
    171 rdf:rest N89865f70ea3642f0a7768e07cfd83dd3
    172 Naffac98083ec4ce482817726f2ea8bb2 rdf:first sg:person.013347016417.86
    173 rdf:rest rdf:nil
    174 Nb88ef2610f02464d87572dc3465d6845 schema:name doi
    175 schema:value 10.1186/gb-2008-9-s2-s9
    176 rdf:type schema:PropertyValue
    177 Nd109b4b696c14ea48844819390cc1c8c rdf:first Nd3fd1d84ceba415a9e0e60c34da97ea2
    178 rdf:rest N4ef3fa4a9b9548ff9d4c2a0a639ecd50
    179 Nd3fd1d84ceba415a9e0e60c34da97ea2 schema:affiliation grid-institutes:grid.266190.a
    180 schema:familyName Lindemann
    181 schema:givenName Anna
    182 rdf:type schema:Person
    183 Ndc3f8259a54f499f9b03cf7df3af1d68 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    184 schema:name Biomedical Research
    185 rdf:type schema:DefinedTerm
    186 Ne17e60a2ab814ae0b7de2a48e469596b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    187 schema:name Protein Interaction Mapping
    188 rdf:type schema:DefinedTerm
    189 Ne810a6c6b2ac4496acdc7656b7956a00 schema:name dimensions_id
    190 schema:value pub.1047888375
    191 rdf:type schema:PropertyValue
    192 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    193 schema:name Information and Computing Sciences
    194 rdf:type schema:DefinedTerm
    195 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    196 schema:name Artificial Intelligence and Image Processing
    197 rdf:type schema:DefinedTerm
    198 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    199 schema:name Information Systems
    200 rdf:type schema:DefinedTerm
    201 sg:grant.2545518 http://pending.schema.org/fundedItem sg:pub.10.1186/gb-2008-9-s2-s9
    202 rdf:type schema:MonetaryGrant
    203 sg:grant.2545550 http://pending.schema.org/fundedItem sg:pub.10.1186/gb-2008-9-s2-s9
    204 rdf:type schema:MonetaryGrant
    205 sg:journal.1023439 schema:issn 1465-6906
    206 1474-760X
    207 schema:name Genome Biology
    208 schema:publisher Springer Nature
    209 rdf:type schema:Periodical
    210 sg:person.01074355715.83 schema:affiliation grid-institutes:grid.266190.a
    211 schema:familyName Lu
    212 schema:givenName Zhiyong
    213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074355715.83
    214 rdf:type schema:Person
    215 sg:person.01155023050.51 schema:affiliation grid-institutes:grid.266190.a
    216 schema:familyName Cohen
    217 schema:givenName K Bretonnel
    218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01155023050.51
    219 rdf:type schema:Person
    220 sg:person.01170711253.75 schema:affiliation grid-institutes:grid.266190.a
    221 schema:familyName White
    222 schema:givenName Elizabeth K
    223 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01170711253.75
    224 rdf:type schema:Person
    225 sg:person.01250122671.15 schema:affiliation grid-institutes:grid.266190.a
    226 schema:familyName Johnson
    227 schema:givenName Helen L
    228 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01250122671.15
    229 rdf:type schema:Person
    230 sg:person.013347016417.86 schema:affiliation grid-institutes:grid.266190.a
    231 schema:familyName Hunter
    232 schema:givenName Lawrence
    233 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013347016417.86
    234 rdf:type schema:Person
    235 sg:person.0601522464.08 schema:affiliation grid-institutes:grid.266190.a
    236 schema:familyName Baumgartner
    237 schema:givenName William A
    238 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601522464.08
    239 rdf:type schema:Person
    240 sg:person.0624224157.70 schema:affiliation grid-institutes:grid.266190.a
    241 schema:familyName Caporaso
    242 schema:givenName J Gregory
    243 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624224157.70
    244 rdf:type schema:Person
    245 sg:pub.10.1007/11573036_36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048946343
    246 https://doi.org/10.1007/11573036_36
    247 rdf:type schema:CreativeWork
    248 sg:pub.10.1038/75556 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044135237
    249 https://doi.org/10.1038/75556
    250 rdf:type schema:CreativeWork
    251 sg:pub.10.1186/1471-2105-6-s1-s11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050375602
    252 https://doi.org/10.1186/1471-2105-6-s1-s11
    253 rdf:type schema:CreativeWork
    254 sg:pub.10.1186/1471-2105-6-s1-s16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038229739
    255 https://doi.org/10.1186/1471-2105-6-s1-s16
    256 rdf:type schema:CreativeWork
    257 sg:pub.10.1186/1471-2105-6-s1-s2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048957855
    258 https://doi.org/10.1186/1471-2105-6-s1-s2
    259 rdf:type schema:CreativeWork
    260 sg:pub.10.1186/1471-2105-6-s1-s4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019731137
    261 https://doi.org/10.1186/1471-2105-6-s1-s4
    262 rdf:type schema:CreativeWork
    263 sg:pub.10.1186/1471-2105-9-78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030172418
    264 https://doi.org/10.1186/1471-2105-9-78
    265 rdf:type schema:CreativeWork
    266 sg:pub.10.1186/1747-5333-1-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046185092
    267 https://doi.org/10.1186/1747-5333-1-3
    268 rdf:type schema:CreativeWork
    269 sg:pub.10.1186/1747-5333-2-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012341552
    270 https://doi.org/10.1186/1747-5333-2-4
    271 rdf:type schema:CreativeWork
    272 sg:pub.10.1186/gb-2008-9-s1-s2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039025062
    273 https://doi.org/10.1186/gb-2008-9-s1-s2
    274 rdf:type schema:CreativeWork
    275 sg:pub.10.1186/gb-2008-9-s1-s3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006959314
    276 https://doi.org/10.1186/gb-2008-9-s1-s3
    277 rdf:type schema:CreativeWork
    278 sg:pub.10.1186/gb-2008-9-s1-s4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024435781
    279 https://doi.org/10.1186/gb-2008-9-s1-s4
    280 rdf:type schema:CreativeWork
    281 sg:pub.10.1186/gb-2008-9-s1-s5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010873868
    282 https://doi.org/10.1186/gb-2008-9-s1-s5
    283 rdf:type schema:CreativeWork
    284 sg:pub.10.1186/gb-2008-9-s2-s2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041411233
    285 https://doi.org/10.1186/gb-2008-9-s2-s2
    286 rdf:type schema:CreativeWork
    287 sg:pub.10.1186/gb-2008-9-s2-s3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004073806
    288 https://doi.org/10.1186/gb-2008-9-s2-s3
    289 rdf:type schema:CreativeWork
    290 sg:pub.10.1186/gb-2008-9-s2-s4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010159966
    291 https://doi.org/10.1186/gb-2008-9-s2-s4
    292 rdf:type schema:CreativeWork
    293 sg:pub.10.1186/gb-2008-9-s2-s5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022366302
    294 https://doi.org/10.1186/gb-2008-9-s2-s5
    295 rdf:type schema:CreativeWork
    296 grid-institutes:grid.266190.a schema:alternateName Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA
    297 schema:name Center for Computational Pharmacology, University of Colorado School of Medicine, 12801 E. 17th Ave Aurora, Colorado, 80045, USA
    298 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...