Open AccessA Data Fusion Approach to Enhance Association Study in Epilepsy
Author(s) -
Simone Marini,
Ivan Limongelli,
Ettore Rizzo,
Alberto Malovini,
Edoardo Errichiello,
Annalisa Vetro,
Tan Da,
Orsetta Zuffardi,
Riccardo Bellazzi
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0164940
Subject(s) - epilepsy , computational biology , computer science , genome wide association study , domain (mathematical analysis) , genetic data , component (thermodynamics) , disease , genetic association , bioinformatics , complex disease , association (psychology) , set (abstract data type) , data mining , medicine , gene , artificial intelligence , biology , genetics , neuroscience , genotype , psychology , pathology , population , mathematics , psychotherapist , mathematical analysis , environmental health , thermodynamics , physics , single nucleotide polymorphism , programming language
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.