Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Li:2016:bdm,

• author = "Ruowang Li and Scott M. Dudek and Dokyoon Kim and Molly A. Hall and Yuki Bradford and Peggy L. Peissig and Murray H. Brilliant and James G. Linneman and Catherine A. McCarty and Le Bao and Marylyn D. Ritchie",
• title = "Identification of genetic interaction networks via an evolutionary algorithm evolved {Bayesian} network",
• journal = "BioData Mining",
• year = "2016",
• volume = "9",
• number = "1",
• pages = "18",
• month = "10 " # may,
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution Bayesian Network (GEBN)",
• ISSN = "1756-0381",
• DOI = "doi:10.1186/s13040-016-0094-4",
• URL = "https://doi.org/10.1186/s13040-016-0094-4",
• language = "en",
• oai = "oai:biomedcentral.com:s13040-016-0094-4",
• URL = "http://www.biodatamining.org/content/9/1/18",
• abstract = "The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analysing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing.",

}

Genetic Programming entries for Ruowang Li Scott M Dudek Dokyoon Kim Molly A Hall Yuki Bradford Peggy L Peissig Murray H Brilliant James G Linneman Catherine A McCarty Le Bao Marylyn D Ritchie

Citations