dgn-np:NP89013.RA91cicWbXfLXeEhgHC-kAh6wry0PL9TsMZHeeZgWZKYs130_provenance {
  dgn-np:NP89013.RA91cicWbXfLXeEhgHC-kAh6wry0PL9TsMZHeeZgWZKYs130_assertion dcterms:description "[This study presents one of the first illustrations of successfully analyzing GWA data with a machine learning algorithm. It is shown that RF is computationally feasible for GWA data and the results obtained make biologic sense based on previous studies. More importantly, new genes were identified as potentially being associated with MS, suggesting new avenues of investigation for this complex disease.]. Sentence from MEDLINE/PubMed, a database of the U.S. National Library of Medicine."@en ;
    wi:evidence dgn-void:source_evidence_literature ;
    sio:SIO_000772 miriam-pubmed:20546594 ;
    prov:wasDerivedFrom dgn-void:gad-20130706 ;
    prov:wasGeneratedBy eco:ECO_0000203 .
  dgn-void:gad-20130706 pav:importedOn "2013-07-06"^^xsd:date .
  dgn-void:source_evidence_literature a eco:ECO_0000212 ;
    rdfs:comment "Gene-disease associations inferred from text-mining the literature."@en ;
    rdfs:label "DisGeNET evidence - LITERATURE"@en .
}