Learning to Combine miRNA Target Predictions: a Semi-supervised Ensemble Learning Approach (Contributo in atti di convegno)

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  • Learning to Combine miRNA Target Predictions: a Semi-supervised Ensemble Learning Approach (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Alternative label
  • Gianvito Pio1, Michelangelo Ceci1, Domenica D'Elia2 and Donato Malerba1 (2014)
    Learning to Combine miRNA Target Predictions: a Semi-supervised Ensemble Learning Approach
    in SEDB 2014 - 22nd Italian Symposium on Advanced Database Systems, Sorrento, Italy, 16-18 June 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gianvito Pio1, Michelangelo Ceci1, Domenica D'Elia2 and Donato Malerba1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 8 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1 University of Bari - Dept. of Computer Science - Via Orabona, 4 - 70125 Bari, Italy 2 ITB-CNR, Via Amendola 122/D, 70126, Bari, Italy (literal)
Titolo
  • Learning to Combine miRNA Target Predictions: a Semi-supervised Ensemble Learning Approach (literal)
Abstract
  • Link prediction in network data is a data mining task which is receiving signicant attention due to its applicability in various domains. An example can be found in social network analysis, where the goal is to identify connections between users. Another application can be found in computational biology, where the goal is to identify previously unknown relationships among biological entities. For example, the identication of regulatory activities (links) among genes would allow biologists to discover possible gene regulatory networks. In the literature, several approaches for link prediction can be found, but they often fail in simultaneously considering all the possible criteria (e.g. network topology, nodes properties, autocorrelation among nodes). In this paper we present a semi-supervised data mining approach which learns to combine the scores returned by several link prediction algorithms. The proposed solution exploits both a small set of validated examples of links and a huge set of unlabeled links. The application we consider regards the iden- tication of links between genes and miRNAs, which can contribute to the understanding of their roles in many biological processes. The specic application requires to learn from only positively labeled examples of links and to face with the high unbalancing between labeled and unlabeled examples. Results show a signicant improvement with respect to single prediction algorithms and with respect to baseline combination. (literal)
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