Mining Frequent Pattern of Biological Spaced Motifs. (Contributo in atti di convegno)

Type
Label
  • Mining Frequent Pattern of Biological Spaced Motifs. (Contributo in atti di convegno) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Alternative label
  • Corrado Loglisci1, Eliana Salvemini1, Antonio Turi1, Giorgio Grillo2, Domenica D'Elia2, Donato Malerba1 (2009)
    Mining Frequent Pattern of Biological Spaced Motifs.
    in Seventeenth Italian Symposium on Advanced Database Systems - SEBD 2009, Camogli (Genova), Italy, 21 - 24 June 2009
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Corrado Loglisci1, Eliana Salvemini1, Antonio Turi1, Giorgio Grillo2, Domenica D'Elia2, Donato Malerba1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://air.unimi.it/handle/2434/153063 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proceedings of the Seventeenth Italian Symposium on Advanced Database Systems - SEBD 2009 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1Dipartimento di Informatica, Universit`a degli Studi di Bari 2Institute for Biomedical Technologies, CNR, Bari (literal)
Titolo
  • Mining Frequent Pattern of Biological Spaced Motifs. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 9788861221543 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autoriVolume
  • V. De Antonellis, S. Castano, B. Catania, G. Guerrini. - Torino : Edizioni Seneca, 2009. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • CASTANO, SILVANA (literal)
Abstract
  • A well-investigated problem in Computational Biology is that of identifying regulatory functions from the genome and an important contribution is the understanding of the nature and spatial location of regulatory motifs. To address this issue, in this paper we apply a computational approach based on data mining techniques. The idea is that of mining frequent combinations of spatially distributed (or spaced) regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the understanding of gene expression. The mining process proceeds in two steps. The first step extracts sets of cooccurring motifs without taking into account their spatial displacement. The second step mines frequent pattern of motifs in the form of association rules by exploiting the previously extracted sets of motifs and by taking into account the spatial information on their inter-motif distance. Experimental results on mRNA untranslated regions prove the effectiveness of the approach to support the task of biologists. (literal)
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