Ontology type: schema:ScholarlyArticle Open Access: True
2014-12
AUTHORSNaomi Kochi, Tomáš Helikar, Laura Allen, Jim A Rogers, Zhenyuan Wang, Mihaela T Matache
ABSTRACTBACKGROUND: An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network. RESULTS: We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling molecules (mainly proteins), the algebraic method provides a robust classification of attribute contributions. This method indicates that for the biochemical network, the most significant impact is generated mainly by the combined effects of two attributes: out-degree, and average sensitivity of nodes. CONCLUSIONS: The results support the idea that both topological and dynamical properties of the nodes need to be under consideration. The algebraic method is robust against the choice of initial conditions and partition of data sets in training and testing sets for estimation of the nonadditive set functions of the information fusion procedure. More... »
PAGES92
http://scigraph.springernature.com/pub.10.1186/s12918-014-0092-4
DOIhttp://dx.doi.org/10.1186/s12918-014-0092-4
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1044763399
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/25189194
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/2008/0601",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Biochemistry and Cell Biology",
"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": "Computer Simulation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Biological",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Protein Interaction Maps",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Signal Transduction",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Systems Biology",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "University of Nebraska Medical Center",
"id": "https://www.grid.ac/institutes/grid.266813.8",
"name": [
"Department of Genetics, Cell Biology, and Anatomy, University of Nebraska Medical Center, 68198, Omaha, NE, USA"
],
"type": "Organization"
},
"familyName": "Kochi",
"givenName": "Naomi",
"id": "sg:person.01074330553.19",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074330553.19"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Nebraska\u2013Lincoln",
"id": "https://www.grid.ac/institutes/grid.24434.35",
"name": [
"Department of Mathematics, University of Nebraska at Omaha, 68182, Omaha, NE, USA",
"Department of Biochemistry, University of Nebraska-Lincoln, 68588, Lincoln, NE, USA"
],
"type": "Organization"
},
"familyName": "Helikar",
"givenName": "Tom\u00e1\u0161",
"id": "sg:person.01210600565.65",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01210600565.65"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Nebraska at Omaha",
"id": "https://www.grid.ac/institutes/grid.266815.e",
"name": [
"Department of Mathematics, University of Nebraska at Omaha, 68182, Omaha, NE, USA"
],
"type": "Organization"
},
"familyName": "Allen",
"givenName": "Laura",
"id": "sg:person.01042024650.29",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01042024650.29"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Nebraska at Omaha",
"id": "https://www.grid.ac/institutes/grid.266815.e",
"name": [
"Department of Mathematics, University of Nebraska at Omaha, 68182, Omaha, NE, USA"
],
"type": "Organization"
},
"familyName": "Rogers",
"givenName": "Jim A",
"id": "sg:person.01146326465.48",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01146326465.48"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Nebraska at Omaha",
"id": "https://www.grid.ac/institutes/grid.266815.e",
"name": [
"Department of Mathematics, University of Nebraska at Omaha, 68182, Omaha, NE, USA"
],
"type": "Organization"
},
"familyName": "Wang",
"givenName": "Zhenyuan",
"id": "sg:person.014741117732.20",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014741117732.20"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Nebraska at Omaha",
"id": "https://www.grid.ac/institutes/grid.266815.e",
"name": [
"Department of Mathematics, University of Nebraska at Omaha, 68182, Omaha, NE, USA"
],
"type": "Organization"
},
"familyName": "Matache",
"givenName": "Mihaela T",
"id": "sg:person.01343251514.83",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01343251514.83"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1159/000327140",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000854128"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0165-0114(89)90194-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002178640"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0165-0114(89)90194-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002178640"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/clpt.2013.41",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003855873",
"https://doi.org/10.1038/clpt.2013.41"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1004678383",
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4757-5303-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004678383",
"https://doi.org/10.1007/978-1-4757-5303-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4757-5303-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004678383",
"https://doi.org/10.1007/978-1-4757-5303-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/18.10.1319",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009537921"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1006/jtbi.2002.3081",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010468039"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pcbi.1000912",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015000430"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1140/epjb/e2013-40009-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016657587",
"https://doi.org/10.1140/epjb/e2013-40009-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1140/epjb/e2010-10559-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017946384",
"https://doi.org/10.1140/epjb/e2010-10559-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1140/epjb/e2010-10559-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017946384",
"https://doi.org/10.1140/epjb/e2010-10559-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1136/hrt.2005.072280",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018021181"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pcbi.1002267",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018228391"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1752-0509-6-96",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021096684",
"https://doi.org/10.1186/1752-0509-6-96"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.1534782100",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022079492"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2164-13-s6-s4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027222819",
"https://doi.org/10.1186/1471-2164-13-s6-s4"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.0407783101",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029639625"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pcbi.1000438",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031533572"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0165-0114(94)90008-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032296051"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0165-0114(94)90008-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032296051"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0061318",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032673634"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.biosystems.2011.12.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034444087"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0061757",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034741313"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/bies.20834",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035597526"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/bts036",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036251835"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreve.72.055101",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036649683"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreve.72.055101",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036649683"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.molcel.2012.11.023",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038594570"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0092-8674(00)00112-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039444748"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pcbi.1000702",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039504491"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1056/nejmoa040938",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042907590"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1742-4682-10-40",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044964621",
"https://doi.org/10.1186/1742-4682-10-40"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.0705088105",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049279196"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1752-0509-3-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049490622",
"https://doi.org/10.1186/1752-0509-3-1"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.0404843101",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051928910"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1015777824097",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052725138",
"https://doi.org/10.1023/a:1015777824097"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/18.2.261",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052758132"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.3225563",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1057920971"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.93.048701",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060828729"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.93.048701",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060828729"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1209/0295-5075/1/2/001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064225392"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.2174/092986707781368423",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1069161005"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1142/6861",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098909744"
],
"type": "CreativeWork"
}
],
"datePublished": "2014-12",
"datePublishedReg": "2014-12-01",
"description": "BACKGROUND: An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network.\nRESULTS: We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling molecules (mainly proteins), the algebraic method provides a robust classification of attribute contributions. This method indicates that for the biochemical network, the most significant impact is generated mainly by the combined effects of two attributes: out-degree, and average sensitivity of nodes.\nCONCLUSIONS: The results support the idea that both topological and dynamical properties of the nodes need to be under consideration. The algebraic method is robust against the choice of initial conditions and partition of data sets in training and testing sets for estimation of the nonadditive set functions of the information fusion procedure.",
"genre": "research_article",
"id": "sg:pub.10.1186/s12918-014-0092-4",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.2487033",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1327442",
"issn": [
"1752-0509"
],
"name": "BMC Systems Biology",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "8"
}
],
"name": "Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions",
"pagination": "92",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"d53965f7c81d8d40a3f04e14e6fcea77ae28621abb54b673cb1b9a7bb38f2b95"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"25189194"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"101301827"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1186/s12918-014-0092-4"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1044763399"
]
}
],
"sameAs": [
"https://doi.org/10.1186/s12918-014-0092-4",
"https://app.dimensions.ai/details/publication/pub.1044763399"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T09:54",
"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/v90/0000000347_0000000347/records_89798_00000002.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1186%2Fs12918-014-0092-4"
}
]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/s12918-014-0092-4'
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/s12918-014-0092-4'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12918-014-0092-4'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12918-014-0092-4'
This table displays all metadata directly associated to this object as RDF triples.
257 TRIPLES
21 PREDICATES
73 URIs
26 LITERALS
14 BLANK NODES