@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix hycl: <http://purl.org/petapico/o/hycl#> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix ns1: <http://purl.org/spar/cito/> .
@prefix ns2: <http://schema.org/> .
@prefix nt: <https://w3id.org/np/o/ntemplate/> .
@prefix orcid: <https://orcid.org/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix sub: <https://w3id.org/np/RAbe1ffda58109e091214d4be1059cc4c20c18495d5ef/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

sub:Head {
  <https://w3id.org/np/RAs1fOQDhSTB_fNKKpVl2hYq7VS6n7gD29_D_5cNQsyok> np:hasAssertion
      sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo;
    a np:Nanopublication .
}

sub:assertion {
  <http://purl.org/aida/Quantum%20approximate%20optimization%20algorithms%20outperform%20classical%20methods%20for%20disjoint%20network%20motif%20identification%20in%20biological%20networks.>
    ns1:obtainsSupportFrom <https://doi.org/10.1093/bioadv/vbae208>;
    ns2:about <http://www.wikidata.org/entity/Q17137535>, <http://www.wikidata.org/entity/Q47536>;
    a hycl:AIDA-Sentence .
}

sub:provenance {
  sub:assertion prov:wasAttributedTo orcid:0000-0002-1784-2920 .
}

sub:pubinfo {
  <http://www.wikidata.org/entity/Q17137535> nt:hasLabelFromApi "network motif" .
  
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  orcid:0000-0002-1784-2920 foaf:name "Anne Fouilloux" .
  
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    dcterms:creator orcid:0000-0002-1784-2920;
    dcterms:license <https://creativecommons.org/licenses/by/4.0/>;
    npx:hasNanopubType hycl:AIDA-Sentence, <http://purl.org/petapico/o/hycl>;
    npx:introduces <http://purl.org/aida/Quantum%20approximate%20optimization%20algorithms%20outperform%20classical%20methods%20for%20disjoint%20network%20motif%20identification%20in%20biological%20networks.>;
    npx:wasCreatedAt <https://nanodash.knowledgepixels.com/>;
    rdfs:label "AIDA sentence: Quantum approximate optimization algorithms outperform classical methods for disjoint network motif ...";
    nt:wasCreatedFromProvenanceTemplate <https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU>;
    nt:wasCreatedFromPubinfoTemplate <https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw>,
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    nt:wasCreatedFromTemplate <https://w3id.org/np/RALmXhDw3rHcMveTgbv8VtWxijUHwnSqhCmtJFIPKWVaA> .
  
  <https://w3id.org/np/RAs1fOQDhSTB_fNKKpVl2hYq7VS6n7gD29_D_5cNQsyok/sig> npx:hasAlgorithm
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    npx:hasSignatureTarget <https://w3id.org/np/RAs1fOQDhSTB_fNKKpVl2hYq7VS6n7gD29_D_5cNQsyok>;
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}