Ontology type: schema:ScholarlyArticle Open Access: True
2006-12
AUTHORSTim Van den Bulcke, Koenraad Van Leemput, Bart Naudts, Piet van Remortel, Hongwu Ma, Alain Verschoren, Bart De Moor, Kathleen Marchal
ABSTRACTBACKGROUND: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner. RESULTS: In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms. CONCLUSION: This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data. More... »
PAGES43
http://scigraph.springernature.com/pub.10.1186/1471-2105-7-43
DOIhttp://dx.doi.org/10.1186/1471-2105-7-43
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1042452525
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/16438721
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/2008/0801",
"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/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Algorithms",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Artificial Intelligence",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Benchmarking",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Computer Simulation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Databases, Factual",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Gene Expression Regulation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Biological",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Signal Transduction",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Software",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Software Validation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Transcription Factors",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "KU Leuven",
"id": "https://www.grid.ac/institutes/grid.5596.f",
"name": [
"ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, B-3001, Heverlee, Belgium"
],
"type": "Organization"
},
"familyName": "Van den Bulcke",
"givenName": "Tim",
"id": "sg:person.01037365125.04",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01037365125.04"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Antwerp",
"id": "https://www.grid.ac/institutes/grid.5284.b",
"name": [
"ISLab, Dept. Math. and Comp. Sc, University of Antwerp, Middelheimlaan 1, B-2020, Antwerpen, Belgium"
],
"type": "Organization"
},
"familyName": "Van Leemput",
"givenName": "Koenraad",
"id": "sg:person.01105500325.86",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01105500325.86"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Antwerp",
"id": "https://www.grid.ac/institutes/grid.5284.b",
"name": [
"ISLab, Dept. Math. and Comp. Sc, University of Antwerp, Middelheimlaan 1, B-2020, Antwerpen, Belgium"
],
"type": "Organization"
},
"familyName": "Naudts",
"givenName": "Bart",
"id": "sg:person.01153613525.80",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01153613525.80"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Antwerp",
"id": "https://www.grid.ac/institutes/grid.5284.b",
"name": [
"ISLab, Dept. Math. and Comp. Sc, University of Antwerp, Middelheimlaan 1, B-2020, Antwerpen, Belgium"
],
"type": "Organization"
},
"familyName": "van Remortel",
"givenName": "Piet",
"id": "sg:person.0710104235.84",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0710104235.84"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "GBF German Research Centre for Biotechnology",
"id": "https://www.grid.ac/institutes/grid.418123.d",
"name": [
"Dept. of Genome Analysis, German Research Center for Biotechnology, Mascheroder Weg 1, D-38124, Braunschweig, Germany"
],
"type": "Organization"
},
"familyName": "Ma",
"givenName": "Hongwu",
"id": "sg:person.01063362515.08",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01063362515.08"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Antwerp",
"id": "https://www.grid.ac/institutes/grid.5284.b",
"name": [
"ISLab, Dept. Math. and Comp. Sc, University of Antwerp, Middelheimlaan 1, B-2020, Antwerpen, Belgium"
],
"type": "Organization"
},
"familyName": "Verschoren",
"givenName": "Alain",
"id": "sg:person.016034265055.79",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016034265055.79"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "KU Leuven",
"id": "https://www.grid.ac/institutes/grid.5596.f",
"name": [
"ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, B-3001, Heverlee, Belgium"
],
"type": "Organization"
},
"familyName": "De Moor",
"givenName": "Bart",
"id": "sg:person.01303274061.35",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01303274061.35"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "KU Leuven",
"id": "https://www.grid.ac/institutes/grid.5596.f",
"name": [
"ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, B-3001, Heverlee, Belgium",
"CMPG, Dept. Microbial and Molecular Systems, K.U.Leuven, Kasteelpark Arenberg 20, B-3001, Heverlee, Belgium"
],
"type": "Organization"
},
"familyName": "Marchal",
"givenName": "Kathleen",
"id": "sg:person.01354214636.95",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354214636.95"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1103/physrevlett.85.5234",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001892823"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.85.5234",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001892823"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.physa.2003.10.083",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010255366"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ng881",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010954918",
"https://doi.org/10.1038/ng881"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ng881",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010954918",
"https://doi.org/10.1038/ng881"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btg1082",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011819441"
],
"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/10.1093/bioinformatics/btg1069",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025271253"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/18.suppl_1.s216",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029007691"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1126/science.298.5594.824",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033238539"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-48304-7_63",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036292845",
"https://doi.org/10.1007/3-540-48304-7_63"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-48304-7_63",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036292845",
"https://doi.org/10.1007/3-540-48304-7_63"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.1230759100",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037459256"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/30918",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041985305",
"https://doi.org/10.1038/30918"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/30918",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041985305",
"https://doi.org/10.1038/30918"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gkh1009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043172753"
],
"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/30.4.e15",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045723576"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ng873",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046040073",
"https://doi.org/10.1038/ng873"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ng873",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046040073",
"https://doi.org/10.1038/ng873"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-6-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048969464",
"https://doi.org/10.1186/1471-2105-6-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-6-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048969464",
"https://doi.org/10.1186/1471-2105-6-8"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/17.suppl_1.s243",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049459826"
],
"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/10.1093/bioinformatics/13.4.377",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050760449"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btg313",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051076828"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1089/106652701753307485",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1059204908"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1074480889",
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1074480891",
"type": "CreativeWork"
}
],
"datePublished": "2006-12",
"datePublishedReg": "2006-12-01",
"description": "BACKGROUND: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner.\nRESULTS: In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms.\nCONCLUSION: This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data.",
"genre": "research_article",
"id": "sg:pub.10.1186/1471-2105-7-43",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1023786",
"issn": [
"1471-2105"
],
"name": "BMC Bioinformatics",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "7"
}
],
"name": "SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms",
"pagination": "43",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"ca389bdad29764601856a45e84468092e93a073eaddf387b1e64fcc44e749ddd"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"16438721"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"100965194"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1186/1471-2105-7-43"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1042452525"
]
}
],
"sameAs": [
"https://doi.org/10.1186/1471-2105-7-43",
"https://app.dimensions.ai/details/publication/pub.1042452525"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T00:14",
"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_8695_00000507.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1186%2F1471-2105-7-43"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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.10.1186/1471-2105-7-43'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-7-43'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-7-43'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-7-43'
This table displays all metadata directly associated to this object as RDF triples.
240 TRIPLES
21 PREDICATES
63 URIs
32 LITERALS
20 BLANK NODES