A Data Fusion Approach to Enhance Association Study in Epilepsy
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...
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| Veröffentlicht in: | PloS one 2016-12, Vol.11 (12), p.e0164940-e0164940 |
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| creator | Marini, Simone Limongelli, Ivan Rizzo, Ettore Malovini, Alberto Errichiello, Edoardo Vetro, Annalisa Da, Tan Zuffardi, Orsetta Bellazzi, Riccardo |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0164940 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0164940</identifier><identifier>PMID: 27984588</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Bioinformatics ; Biology and Life Sciences ; Biomedical engineering ; Computational Biology - methods ; Data integration ; Epilepsy ; Epilepsy - genetics ; Gene Regulatory Networks ; Gene sequencing ; Genes ; Genetic aspects ; Genetic Association Studies - methods ; Genetic diversity ; Genetic Predisposition to Disease ; Genetic susceptibility ; Genetic variance ; Genetic Variation ; Genetics ; Genomes ; High-Throughput Nucleotide Sequencing - methods ; Humans ; Medicine and Health Sciences ; Methods ; Multisensor fusion ; Multivariate analysis ; Mutation ; Physical Sciences ; Power ; Prediction models ; Protein interaction ; Protein Interaction Maps ; Protein-protein interactions ; Proteins ; Research and Analysis Methods ; Risk factors ; Social Sciences ; Studies</subject><ispartof>PloS one, 2016-12, Vol.11 (12), p.e0164940-e0164940</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Marini et al. 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27984588</pmid><doi>10.1371/journal.pone.0164940</doi><tpages>e0164940</tpages><orcidid>https://orcid.org/0000-0002-5704-3533</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Bioinformatics Biology and Life Sciences Biomedical engineering Computational Biology - methods Data integration Epilepsy Epilepsy - genetics Gene Regulatory Networks Gene sequencing Genes Genetic aspects Genetic Association Studies - methods Genetic diversity Genetic Predisposition to Disease Genetic susceptibility Genetic variance Genetic Variation Genetics Genomes High-Throughput Nucleotide Sequencing - methods Humans Medicine and Health Sciences Methods Multisensor fusion Multivariate analysis Mutation Physical Sciences Power Prediction models Protein interaction Protein Interaction Maps Protein-protein interactions Proteins Research and Analysis Methods Risk factors Social Sciences Studies |
| title | A Data Fusion Approach to Enhance Association Study in Epilepsy |
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