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.
MeSH terms
-
Algorithms
-
Computational Biology / methods*
-
Epilepsy / genetics*
-
Gene Regulatory Networks
-
Genetic Association Studies / methods*
-
Genetic Predisposition to Disease
-
Genetic Variation
-
High-Throughput Nucleotide Sequencing / methods
-
Humans
-
Protein Interaction Maps
Grants and funding
AV benefits of a research position granted by the University of Pavia in the context of the strategic plan: "Towards a governance model for the international migration: an interdisciplinary and diachronic perspective"; OZ was supported by Telethon Italy Grant GGP13060. SM is an International Research Fellow of the Japan Society for the Promotion of Science. The work was partly supported by the strategic plan of the University of Pavia: “Centre for Health Technologies". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.