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A Data Fusion Approach to Enhance Association Study in Epilepsy

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  • Simone Marini
  • Ivan Limongelli
  • Ettore Rizzo
  • Alberto Malovini
  • Edoardo Errichiello
  • Annalisa Vetro
  • Tan Da
  • Orsetta Zuffardi
  • Riccardo Bellazzi

Abstract

Among the scientific challenges posed by complex diseases with a strong genetic component, two stand out. One is unveiling the role of rare and common genetic variants; the other is the design of classification models to improve clinical diagnosis and predictive models for prognosis and personalized therapies. In this paper, we present a data fusion framework merging gene, domain, pathway and protein-protein interaction data related to a next generation sequencing epilepsy gene panel. Our method allows integrating association information from multiple genomic sources and aims at highlighting the set of common and rare variants that are capable to trigger the occurrence of a complex disease. When compared to other approaches, our method shows better performances in classifying patients affected by epilepsy.

Suggested Citation

  • Simone Marini & Ivan Limongelli & Ettore Rizzo & Alberto Malovini & Edoardo Errichiello & Annalisa Vetro & Tan Da & Orsetta Zuffardi & Riccardo Bellazzi, 2016. "A Data Fusion Approach to Enhance Association Study in Epilepsy," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0164940
    DOI: 10.1371/journal.pone.0164940
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    References listed on IDEAS

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    1. Geraldine M Clarke & Manuel A Rivas & Andrew P Morris, 2013. "A Flexible Approach for the Analysis of Rare Variants Allowing for a Mixture of Effects on Binary or Quantitative Traits," PLOS Genetics, Public Library of Science, vol. 9(8), pages 1-8, August.
    2. Li Ma & Andrew G Clark & Alon Keinan, 2013. "Gene-Based Testing of Interactions in Association Studies of Quantitative Traits," PLOS Genetics, Public Library of Science, vol. 9(2), pages 1-12, February.
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