Integrated analysis of genetic, genomic, and proteomic data

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@Article{reif:2004:ERP,
  author =       "David M. Reif and Bill C. White and Jason H. Moore",
  title =        "Integrated analysis of genetic, genomic, and proteomic
                 data",
  journal =      "Expert Review of Proteomics",
  year =         "2004",
  volume =       "1",
  number =       "1",
  pages =        "67--75",
  keywords =     "genetic algorithms, genetic programming, SNP, gene
                 expression, protein expression, complex systems,
                 simulation, joint evaluation, data reliability, single
                 nucleotide polymorphism",
  ISSN =         "1473-7159",
  URL =          "http://www.future-drugs.com/doi/abs/10.1586/14789450.1.1.67",
  DOI =          "doi:10.1586/14789450.1.1.67",
  size =         "9 pages",
  abstract =     "The rapid expansion of methods for measuring
                 biological data ranging from DNA sequence variations to
                 mRNA expression and protein abundance presents the
                 opportunity to use multiple types of information
                 jointly in the study of human health and disease.
                 Organisms are complex systems that integrate inputs at
                 myriad levels to arrive at an observable phenotype.
                 Therefore, it is essential that questions concerning
                 the etiology of phenotypes as complex as common human
                 diseases take the systemic nature of biology into
                 account, and integrate the information provided by each
                 data type in a manner analogous to the operation of the
                 body itself. While limited in scope, the initial forays
                 into the joint analysis of multiple data types have
                 yielded interesting results that would not have been
                 reached had only one type of data been considered.
                 These early successes, along with the aforementioned
                 theoretical appeal of data integration, provide impetus
                 for the development of methods for the parallel,
                 high-throughput analysis of multiple data types. The
                 idea that the integrated analysis of multiple data
                 types will improve the identification of biomarkers of
                 clinical endpoints, such as disease susceptibility, is
                 presented as a working hypothesis.",
  notes =        "http://www.future-drugs.com/loi/epr?cookieSet=1

                 PMID: 15966800 [PubMed]",
}

Genetic Programming entries for David M Reif Bill C White Jason H Moore

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