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pubmed-article:18157916pubmed:abstractTextAssessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations.lld:pubmed
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pubmed-article:18157916pubmed:issn1748-7188lld:pubmed
pubmed-article:18157916pubmed:authorpubmed-author:FerreiraPedro...lld:pubmed
pubmed-article:18157916pubmed:authorpubmed-author:AzevedoPaulo...lld:pubmed
pubmed-article:18157916pubmed:issnTypeElectroniclld:pubmed
pubmed-article:18157916pubmed:volume2lld:pubmed
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pubmed-article:18157916pubmed:pagination16lld:pubmed
pubmed-article:18157916pubmed:dateRevised2009-11-18lld:pubmed
pubmed-article:18157916pubmed:year2007lld:pubmed
pubmed-article:18157916pubmed:articleTitleEvaluating deterministic motif significance measures in protein databases.lld:pubmed
pubmed-article:18157916pubmed:affiliationDepartment of Informatics, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal. pedrogabriel@di.uminho.ptlld:pubmed
pubmed-article:18157916pubmed:publicationTypeJournal Articlelld:pubmed