Network reconstruction for the identification of miRNA:mRNA interaction networks (Comunicazione a convegno)

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Label
  • Network reconstruction for the identification of miRNA:mRNA interaction networks (Comunicazione a convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Alternative label
  • Gianvito Pio, Michelangelo Ceci, Domenica D'Elia, Donato Malerba (2014)
    Network reconstruction for the identification of miRNA:mRNA interaction networks
    in The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, September 15th to 19th, 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gianvito Pio, Michelangelo Ceci, Domenica D'Elia, Donato Malerba (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases is the 7th European machine learning and data mining conference and builds upon a very successful series of 7 ECML/PKDD, 18 ECML and 11 PKDD conferences, which have been jointly organized for the past 13 years. The Paper ID = 678: \"Network reconstruction for the identification of miRNA:mRNA interaction networks\" has been accepted for presentation and inclusion in the conference proceedings Springer Publication: \"Machine Learning and Knowledge Discovery in Databases\" proceedings (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ecmlpkdd2014.loria.fr/program/nectar-track-accepted-papers/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Nectar Track Accepted Papers - Nectar Session: 4 | Room: 103-104 | Time: 14:20 - 14:50, Thursday 18 Sept (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Gianvito Pio, University of Bari Michelangelo Ceci, University of Bari Domenica D'Elia, ITB-CNR, Bari Donato Malerba, University of Bari (literal)
Titolo
  • Network reconstruction for the identification of miRNA:mRNA interaction networks (literal)
Abstract
  • Network reconstruction from data is a data mining task which is receiving a significant attention due to its applicability in several domains. For example, it can be applied in social network analysis, where the goal is to identify connections among users and, thus, sub-communities. Another example can be found in computational biology, where the goal is to identify previously unknown relationships among biological entities and, thus, relevant interaction networks. Such task is usually solved by adopting methods for link prediction and for the identification of relevant sub-networks. Focusing on the biological domain we proposed two methods for learning to combine the output of several link prediction algorithms and for the identification of biological significant interaction networks involving two important types of RNA molecules, i.e. microRNAs (miRNAs) and messenger RNAs (mRNAs). The relevance of this application comes from the importance of identifying (previously unknown) regulatory and cooperation activities for the understanding of the biological roles of miRNAs and mRNAs. In this paper, we review the contribution given by the combination of the proposed methods for network reconstruction and the solutions we adopt in order to meet specific challenges coming from the specific domain we consider. (literal)
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