A Data Fusion Approach to Enhance Association Study in Epilepsy

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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article numbere0164940
JournalPLoS One
Volume11
Issue number12
DOIs
Publication statusPublished - Dec 1 2016

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ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Marini, S., Limongelli, I., Rizzo, E., Malovini, A., Errichiello, E., Vetro, A., Da, T., Zuffardi, O., & Bellazzi, R. (2016). A Data Fusion Approach to Enhance Association Study in Epilepsy. PLoS One, 11(12), [e0164940]. https://doi.org/10.1371/journal.pone.0164940