From gene expression to gene regulatory networks in Arabidopsis thaliana View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-12

AUTHORS

Chris J Needham, Iain W Manfield, Andrew J Bulpitt, Philip M Gilmartin, David R Westhead

ABSTRACT

BACKGROUND: The elucidation of networks from a compendium of gene expression data is one of the goals of systems biology and can be a valuable source of new hypotheses for experimental researchers. For Arabidopsis, there exist several thousand microarrays which form a valuable resource from which to learn. RESULTS: A novel Bayesian network-based algorithm to infer gene regulatory networks from gene expression data is introduced and applied to learn parts of the transcriptomic network in Arabidopsis thaliana from a large number (thousands) of separate microarray experiments. Starting from an initial set of genes of interest, a network is grown by iterative addition to the model of the gene, from another defined set of genes, which gives the 'best' learned network structure. The gene set for iterative growth can be as large as the entire genome. A number of networks are inferred and analysed; these show (i) an agreement with the current literature on the circadian clock network, (ii) the ability to model other networks, and (iii) that the learned network hypotheses can suggest new roles for poorly characterized genes, through addition of relevant genes from an unconstrained list of over 15,000 possible genes. To demonstrate the latter point, the method is used to suggest that particular GATA transcription factors are regulators of photosynthetic genes. Additionally, the performance in recovering a known network from different amounts of synthetically generated data is evaluated. CONCLUSION: Our results show that plausible regulatory networks can be learned from such gene expression data alone. This work demonstrates that network hypotheses can be generated from existing gene expression data for use by experimental biologists. More... »

PAGES

85

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-3-85

DOI

http://dx.doi.org/10.1186/1752-0509-3-85

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1000349163

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19728870


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/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Arabidopsis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Arabidopsis Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Regulation, Plant", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Biological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pattern Recognition, Automated", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Signal Transduction", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Leeds", 
          "id": "https://www.grid.ac/institutes/grid.9909.9", 
          "name": [
            "School of Computing, University of Leeds, LS2 9JT, Leeds, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Needham", 
        "givenName": "Chris J", 
        "id": "sg:person.0715155432.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715155432.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Leeds", 
          "id": "https://www.grid.ac/institutes/grid.9909.9", 
          "name": [
            "Institute of Integrative and Comparative Biology, University of Leeds, LS2 9JT, Leeds, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Manfield", 
        "givenName": "Iain W", 
        "id": "sg:person.01056441710.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01056441710.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Leeds", 
          "id": "https://www.grid.ac/institutes/grid.9909.9", 
          "name": [
            "School of Computing, University of Leeds, LS2 9JT, Leeds, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bulpitt", 
        "givenName": "Andrew J", 
        "id": "sg:person.0740447261.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0740447261.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Durham University", 
          "id": "https://www.grid.ac/institutes/grid.8250.f", 
          "name": [
            "Institute of Integrative and Comparative Biology, University of Leeds, LS2 9JT, Leeds, UK", 
            "School of Biological and Biomedical Sciences, Durham University, Durham, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gilmartin", 
        "givenName": "Philip M", 
        "id": "sg:person.01106310707.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106310707.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Leeds", 
          "id": "https://www.grid.ac/institutes/grid.9909.9", 
          "name": [
            "Institute of Molecular and Cellular Biology, University of Leeds, LS2 9JT, Leeds, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Westhead", 
        "givenName": "David R", 
        "id": "sg:person.01006562461.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01006562461.17"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1126/science.1105809", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002199042"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2000.00936.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003073864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2000.00936.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003073864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1543", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005385099", 
          "https://doi.org/10.1038/ng1543"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1543", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005385099", 
          "https://doi.org/10.1038/ng1543"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1105/tpc.105.038315", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007732235"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2005.02568.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008737574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2005.02568.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008737574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jtbi.2004.11.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008849505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.0030206", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009206290"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1105/tpc.105.039990", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009234833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010880195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010880195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1532", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012406364", 
          "https://doi.org/10.1038/ng1532"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1532", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012406364", 
          "https://doi.org/10.1038/ng1532"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btk046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013118634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017037753"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017037753"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018491576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018491576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/17.suppl_1.s215", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020864922"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2006.02681.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021412182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2006.02681.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021412182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bti1137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024652155"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1105/tpc.106.040980", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024809406"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1752-0509-1-11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024995093", 
          "https://doi.org/10.1186/1752-0509-1-11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.pbi.2006.11.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027164338"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btl391", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028321425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1534-5807(02)00170-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030692764"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bti062", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031236162"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1104/pp.104.058354", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032943623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.cellbio.24.110707.175408", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034375480"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1399-3054.2005.00592.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035087608"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkl204", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035329961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btl396", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036183277"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1104/pp.107.096206", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036345146"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1104/pp.106.090761", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041691452"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1104/pp.107.115634", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041946875"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1105/tpc.104.024869", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043159606"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043167372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043167372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043167372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.0030129", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044189257"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1094068", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045090769"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkh133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046446976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2005.02588.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049071062"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-313x.2005.02588.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049071062"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/332306.332355", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049104539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/332306.332355", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049104539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-313x.2001.01003.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049172629"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bth941", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050276898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-3040.2006.01627.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051266649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-s1-s7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051833905", 
          "https://doi.org/10.1186/1471-2105-7-s1-s7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-313x.2000.00936.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056738006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s021972000400048x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063004538"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2347605", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101983331"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2347605", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101983331"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-12", 
    "datePublishedReg": "2009-12-01", 
    "description": "BACKGROUND: The elucidation of networks from a compendium of gene expression data is one of the goals of systems biology and can be a valuable source of new hypotheses for experimental researchers. For Arabidopsis, there exist several thousand microarrays which form a valuable resource from which to learn.\nRESULTS: A novel Bayesian network-based algorithm to infer gene regulatory networks from gene expression data is introduced and applied to learn parts of the transcriptomic network in Arabidopsis thaliana from a large number (thousands) of separate microarray experiments. Starting from an initial set of genes of interest, a network is grown by iterative addition to the model of the gene, from another defined set of genes, which gives the 'best' learned network structure. The gene set for iterative growth can be as large as the entire genome. A number of networks are inferred and analysed; these show (i) an agreement with the current literature on the circadian clock network, (ii) the ability to model other networks, and (iii) that the learned network hypotheses can suggest new roles for poorly characterized genes, through addition of relevant genes from an unconstrained list of over 15,000 possible genes. To demonstrate the latter point, the method is used to suggest that particular GATA transcription factors are regulators of photosynthetic genes. Additionally, the performance in recovering a known network from different amounts of synthetically generated data is evaluated.\nCONCLUSION: Our results show that plausible regulatory networks can be learned from such gene expression data alone. This work demonstrates that network hypotheses can be generated from existing gene expression data for use by experimental biologists.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1752-0509-3-85", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1327442", 
        "issn": [
          "1752-0509"
        ], 
        "name": "BMC Systems Biology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "3"
      }
    ], 
    "name": "From gene expression to gene regulatory networks in Arabidopsis thaliana", 
    "pagination": "85", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "01a97a02777db33fe0add78347d2e3faeb659348deeb8e2652b16141074eaf72"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "19728870"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101301827"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1752-0509-3-85"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1000349163"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1752-0509-3-85", 
      "https://app.dimensions.ai/details/publication/pub.1000349163"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:36", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8687_00000509.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2F1752-0509-3-85"
  }
]
 

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.10.1186/1752-0509-3-85'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-3-85'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-3-85'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-3-85'


 

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

274 TRIPLES      21 PREDICATES      82 URIs      30 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1752-0509-3-85 schema:about N2afc6327d6954da9ad1da5c65c453003
2 N2da68256df1147129f8f19eefb6e01b8
3 N35cfb80801944b7a91aca86975228f92
4 N41b5b2aca9a84a9f87e801ce2d36f661
5 N43db28cd32474509bc3081ce8a96204d
6 N7dd5565db5e6460a8757040043356297
7 N9170a4978f2d421bbc1af8db17cd468e
8 N9761caf03e4b4eefba6126a0fd0fbbf0
9 Nbb302f4d8e0e4446b143a145d0122e4f
10 anzsrc-for:06
11 anzsrc-for:0604
12 schema:author N1e7aa8f73a7f4599bee11978d33ffab3
13 schema:citation sg:pub.10.1038/ng1532
14 sg:pub.10.1038/ng1543
15 sg:pub.10.1186/1471-2105-7-s1-s7
16 sg:pub.10.1186/1752-0509-1-11
17 https://doi.org/10.1016/j.jtbi.2004.11.038
18 https://doi.org/10.1016/j.pbi.2006.11.008
19 https://doi.org/10.1016/s1534-5807(02)00170-3
20 https://doi.org/10.1038/msb4100018
21 https://doi.org/10.1038/msb4100101
22 https://doi.org/10.1038/msb4100102
23 https://doi.org/10.1038/msb4100120
24 https://doi.org/10.1046/j.1365-313x.2000.00936.x
25 https://doi.org/10.1046/j.1365-313x.2001.01003.x
26 https://doi.org/10.1093/bioinformatics/17.suppl_1.s215
27 https://doi.org/10.1093/bioinformatics/bth941
28 https://doi.org/10.1093/bioinformatics/bti062
29 https://doi.org/10.1093/bioinformatics/bti1137
30 https://doi.org/10.1093/bioinformatics/btk046
31 https://doi.org/10.1093/bioinformatics/btl391
32 https://doi.org/10.1093/bioinformatics/btl396
33 https://doi.org/10.1093/nar/gkh133
34 https://doi.org/10.1093/nar/gkl204
35 https://doi.org/10.1104/pp.104.058354
36 https://doi.org/10.1104/pp.106.090761
37 https://doi.org/10.1104/pp.107.096206
38 https://doi.org/10.1104/pp.107.115634
39 https://doi.org/10.1105/tpc.104.024869
40 https://doi.org/10.1105/tpc.105.038315
41 https://doi.org/10.1105/tpc.105.039990
42 https://doi.org/10.1105/tpc.106.040980
43 https://doi.org/10.1111/j.1365-3040.2006.01627.x
44 https://doi.org/10.1111/j.1365-313x.2000.00936.x
45 https://doi.org/10.1111/j.1365-313x.2005.02568.x
46 https://doi.org/10.1111/j.1365-313x.2005.02588.x
47 https://doi.org/10.1111/j.1365-313x.2006.02681.x
48 https://doi.org/10.1111/j.1399-3054.2005.00592.x
49 https://doi.org/10.1126/science.1094068
50 https://doi.org/10.1126/science.1105809
51 https://doi.org/10.1142/s021972000400048x
52 https://doi.org/10.1145/332306.332355
53 https://doi.org/10.1146/annurev.cellbio.24.110707.175408
54 https://doi.org/10.1371/journal.pcbi.0030129
55 https://doi.org/10.1371/journal.pcbi.0030206
56 https://doi.org/10.2307/2347605
57 schema:datePublished 2009-12
58 schema:datePublishedReg 2009-12-01
59 schema:description BACKGROUND: The elucidation of networks from a compendium of gene expression data is one of the goals of systems biology and can be a valuable source of new hypotheses for experimental researchers. For Arabidopsis, there exist several thousand microarrays which form a valuable resource from which to learn. RESULTS: A novel Bayesian network-based algorithm to infer gene regulatory networks from gene expression data is introduced and applied to learn parts of the transcriptomic network in Arabidopsis thaliana from a large number (thousands) of separate microarray experiments. Starting from an initial set of genes of interest, a network is grown by iterative addition to the model of the gene, from another defined set of genes, which gives the 'best' learned network structure. The gene set for iterative growth can be as large as the entire genome. A number of networks are inferred and analysed; these show (i) an agreement with the current literature on the circadian clock network, (ii) the ability to model other networks, and (iii) that the learned network hypotheses can suggest new roles for poorly characterized genes, through addition of relevant genes from an unconstrained list of over 15,000 possible genes. To demonstrate the latter point, the method is used to suggest that particular GATA transcription factors are regulators of photosynthetic genes. Additionally, the performance in recovering a known network from different amounts of synthetically generated data is evaluated. CONCLUSION: Our results show that plausible regulatory networks can be learned from such gene expression data alone. This work demonstrates that network hypotheses can be generated from existing gene expression data for use by experimental biologists.
60 schema:genre research_article
61 schema:inLanguage en
62 schema:isAccessibleForFree true
63 schema:isPartOf N57c817378ae54b9cb6cec5733b8b0dda
64 N8f2ca0d16fab43b0a7f75915c1ccff58
65 sg:journal.1327442
66 schema:name From gene expression to gene regulatory networks in Arabidopsis thaliana
67 schema:pagination 85
68 schema:productId N5e7a274a9eff49faae6ba0a9f25876c8
69 N8ab73e5460884525801620f56e1eab14
70 N8c4a22f62ca347f9a5c8bdd9fff09c8f
71 N8d42ce8ef148479db10a3c9b1ce60e91
72 Ne868351fff6e4305a837dd7ed68b58ef
73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000349163
74 https://doi.org/10.1186/1752-0509-3-85
75 schema:sdDatePublished 2019-04-10T21:36
76 schema:sdLicense https://scigraph.springernature.com/explorer/license/
77 schema:sdPublisher N3f3f56d04059446982c5e4f5d53fe895
78 schema:url http://link.springer.com/10.1186%2F1752-0509-3-85
79 sgo:license sg:explorer/license/
80 sgo:sdDataset articles
81 rdf:type schema:ScholarlyArticle
82 N1e7aa8f73a7f4599bee11978d33ffab3 rdf:first sg:person.0715155432.85
83 rdf:rest N4257c22592fa4bdfb6daf83e60919a76
84 N2afc6327d6954da9ad1da5c65c453003 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Models, Biological
86 rdf:type schema:DefinedTerm
87 N2da68256df1147129f8f19eefb6e01b8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Algorithms
89 rdf:type schema:DefinedTerm
90 N35cfb80801944b7a91aca86975228f92 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Gene Expression Regulation, Plant
92 rdf:type schema:DefinedTerm
93 N36dfb462171148aa9fa8026c86bdd8b0 rdf:first sg:person.01006562461.17
94 rdf:rest rdf:nil
95 N3f3f56d04059446982c5e4f5d53fe895 schema:name Springer Nature - SN SciGraph project
96 rdf:type schema:Organization
97 N41b5b2aca9a84a9f87e801ce2d36f661 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name Pattern Recognition, Automated
99 rdf:type schema:DefinedTerm
100 N4257c22592fa4bdfb6daf83e60919a76 rdf:first sg:person.01056441710.16
101 rdf:rest Nb08bc4f5492e43b29361ccb57e08d8b2
102 N43db28cd32474509bc3081ce8a96204d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Arabidopsis Proteins
104 rdf:type schema:DefinedTerm
105 N57c817378ae54b9cb6cec5733b8b0dda schema:issueNumber 1
106 rdf:type schema:PublicationIssue
107 N5e7a274a9eff49faae6ba0a9f25876c8 schema:name nlm_unique_id
108 schema:value 101301827
109 rdf:type schema:PropertyValue
110 N7dd5565db5e6460a8757040043356297 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Signal Transduction
112 rdf:type schema:DefinedTerm
113 N8ab73e5460884525801620f56e1eab14 schema:name doi
114 schema:value 10.1186/1752-0509-3-85
115 rdf:type schema:PropertyValue
116 N8c4a22f62ca347f9a5c8bdd9fff09c8f schema:name readcube_id
117 schema:value 01a97a02777db33fe0add78347d2e3faeb659348deeb8e2652b16141074eaf72
118 rdf:type schema:PropertyValue
119 N8d42ce8ef148479db10a3c9b1ce60e91 schema:name dimensions_id
120 schema:value pub.1000349163
121 rdf:type schema:PropertyValue
122 N8f2ca0d16fab43b0a7f75915c1ccff58 schema:volumeNumber 3
123 rdf:type schema:PublicationVolume
124 N9170a4978f2d421bbc1af8db17cd468e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Arabidopsis
126 rdf:type schema:DefinedTerm
127 N925e2c5e6360470c9136240caaaf9ee1 rdf:first sg:person.01106310707.52
128 rdf:rest N36dfb462171148aa9fa8026c86bdd8b0
129 N9761caf03e4b4eefba6126a0fd0fbbf0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Gene Expression Profiling
131 rdf:type schema:DefinedTerm
132 Nb08bc4f5492e43b29361ccb57e08d8b2 rdf:first sg:person.0740447261.15
133 rdf:rest N925e2c5e6360470c9136240caaaf9ee1
134 Nbb302f4d8e0e4446b143a145d0122e4f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Computer Simulation
136 rdf:type schema:DefinedTerm
137 Ne868351fff6e4305a837dd7ed68b58ef schema:name pubmed_id
138 schema:value 19728870
139 rdf:type schema:PropertyValue
140 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
141 schema:name Biological Sciences
142 rdf:type schema:DefinedTerm
143 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
144 schema:name Genetics
145 rdf:type schema:DefinedTerm
146 sg:journal.1327442 schema:issn 1752-0509
147 schema:name BMC Systems Biology
148 rdf:type schema:Periodical
149 sg:person.01006562461.17 schema:affiliation https://www.grid.ac/institutes/grid.9909.9
150 schema:familyName Westhead
151 schema:givenName David R
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01006562461.17
153 rdf:type schema:Person
154 sg:person.01056441710.16 schema:affiliation https://www.grid.ac/institutes/grid.9909.9
155 schema:familyName Manfield
156 schema:givenName Iain W
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01056441710.16
158 rdf:type schema:Person
159 sg:person.01106310707.52 schema:affiliation https://www.grid.ac/institutes/grid.8250.f
160 schema:familyName Gilmartin
161 schema:givenName Philip M
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106310707.52
163 rdf:type schema:Person
164 sg:person.0715155432.85 schema:affiliation https://www.grid.ac/institutes/grid.9909.9
165 schema:familyName Needham
166 schema:givenName Chris J
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715155432.85
168 rdf:type schema:Person
169 sg:person.0740447261.15 schema:affiliation https://www.grid.ac/institutes/grid.9909.9
170 schema:familyName Bulpitt
171 schema:givenName Andrew J
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0740447261.15
173 rdf:type schema:Person
174 sg:pub.10.1038/ng1532 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012406364
175 https://doi.org/10.1038/ng1532
176 rdf:type schema:CreativeWork
177 sg:pub.10.1038/ng1543 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005385099
178 https://doi.org/10.1038/ng1543
179 rdf:type schema:CreativeWork
180 sg:pub.10.1186/1471-2105-7-s1-s7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051833905
181 https://doi.org/10.1186/1471-2105-7-s1-s7
182 rdf:type schema:CreativeWork
183 sg:pub.10.1186/1752-0509-1-11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024995093
184 https://doi.org/10.1186/1752-0509-1-11
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1016/j.jtbi.2004.11.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008849505
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1016/j.pbi.2006.11.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027164338
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/s1534-5807(02)00170-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030692764
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1038/msb4100018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043167372
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1038/msb4100101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017037753
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1038/msb4100102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010880195
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1038/msb4100120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018491576
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1046/j.1365-313x.2000.00936.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1056738006
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1046/j.1365-313x.2001.01003.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1049172629
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1093/bioinformatics/17.suppl_1.s215 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020864922
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1093/bioinformatics/bth941 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050276898
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1093/bioinformatics/bti062 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031236162
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1093/bioinformatics/bti1137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024652155
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1093/bioinformatics/btk046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013118634
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1093/bioinformatics/btl391 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028321425
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1093/bioinformatics/btl396 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036183277
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1093/nar/gkh133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046446976
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1093/nar/gkl204 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035329961
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1104/pp.104.058354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032943623
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1104/pp.106.090761 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041691452
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1104/pp.107.096206 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036345146
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1104/pp.107.115634 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041946875
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1105/tpc.104.024869 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043159606
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1105/tpc.105.038315 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007732235
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1105/tpc.105.039990 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009234833
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1105/tpc.106.040980 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024809406
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1111/j.1365-3040.2006.01627.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051266649
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1111/j.1365-313x.2000.00936.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1003073864
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1111/j.1365-313x.2005.02568.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1008737574
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1111/j.1365-313x.2005.02588.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1049071062
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1111/j.1365-313x.2006.02681.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1021412182
247 rdf:type schema:CreativeWork
248 https://doi.org/10.1111/j.1399-3054.2005.00592.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1035087608
249 rdf:type schema:CreativeWork
250 https://doi.org/10.1126/science.1094068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045090769
251 rdf:type schema:CreativeWork
252 https://doi.org/10.1126/science.1105809 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002199042
253 rdf:type schema:CreativeWork
254 https://doi.org/10.1142/s021972000400048x schema:sameAs https://app.dimensions.ai/details/publication/pub.1063004538
255 rdf:type schema:CreativeWork
256 https://doi.org/10.1145/332306.332355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049104539
257 rdf:type schema:CreativeWork
258 https://doi.org/10.1146/annurev.cellbio.24.110707.175408 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034375480
259 rdf:type schema:CreativeWork
260 https://doi.org/10.1371/journal.pcbi.0030129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044189257
261 rdf:type schema:CreativeWork
262 https://doi.org/10.1371/journal.pcbi.0030206 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009206290
263 rdf:type schema:CreativeWork
264 https://doi.org/10.2307/2347605 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101983331
265 rdf:type schema:CreativeWork
266 https://www.grid.ac/institutes/grid.8250.f schema:alternateName Durham University
267 schema:name Institute of Integrative and Comparative Biology, University of Leeds, LS2 9JT, Leeds, UK
268 School of Biological and Biomedical Sciences, Durham University, Durham, UK
269 rdf:type schema:Organization
270 https://www.grid.ac/institutes/grid.9909.9 schema:alternateName University of Leeds
271 schema:name Institute of Integrative and Comparative Biology, University of Leeds, LS2 9JT, Leeds, UK
272 Institute of Molecular and Cellular Biology, University of Leeds, LS2 9JT, Leeds, UK
273 School of Computing, University of Leeds, LS2 9JT, Leeds, UK
274 rdf:type schema:Organization
 




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


...