Comparison of methods for meta-dimensional data analysis using in silico and biological data sets

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{holzinger:evobio12,
  author =       "Emily R. Holzinger and Scott M. Dudek and 
                 Alex T. Frase and Brooke Fridley and Prabhakar Chalise and 
                 Marylyn D. Ritchie",
  title =        "Comparison of methods for meta-dimensional data
                 analysis using in silico and biological data sets",
  booktitle =    "10th European Conference on Evolutionary Computation,
                 Machine Learning and Data Mining in Bioinformatics,
                 {EvoBIO 2012}",
  year =         "2012",
  month =        "11-13 " # apr,
  editor =       "Mario Giacobini and Leonardo Vanneschi and 
                 William S. Bush",
  series =       "LNCS",
  volume =       "7246",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  pages =        "134--143",
  organisation = "EvoStar",
  isbn13 =       "978-3-642-29065-7",
  DOI =          "doi:10.1007/978-3-642-29066-4_12",
  size =         "10 pages",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, GENN, Systems biology, neural networks,
                 evolutionary computation, data integration, human
                 genetics",
  abstract =     "Recent technological innovations have catalysed the
                 generation of a massive amount of data at various
                 levels of biological regulation, including DNA, RNA and
                 protein. Due to the complex nature of biology, the
                 underlying model may only be discovered by integrating
                 different types of high-throughput data to perform a
                 'meta-dimensional' analysis. For this study, we used
                 simulated gene expression and genotype data to compare
                 three methods that show potential for integrating
                 different types of data in order to generate models
                 that predict a given phenotype: the Analysis Tool for
                 Heritable and Environmental Network Associations
                 (ATHENA), Random Jungle (RJ), and Lasso. Based on our
                 results, we applied RJ and ATHENA sequentially to a
                 biological data set that consisted of genome-wide
                 genotypes and gene expression levels from
                 lymphoblastoid cell lines (LCLs) to predict
                 cytotoxicity. The best model consisted of two SNPs and
                 two gene expression variables with an r-squared value
                 of 0.32.",
  notes =        "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held
                 in conjunction with EuroGP2012, EvoCOP2012,
                 EvoMusArt2012 and EvoApplications2012",
  affiliation =  "Center for Human Genetics Research, Vanderbilt
                 University, Nashville, TN, USA",
}

Genetic Programming entries for Emily Rose Holzinger Scott M Dudek Alex T Frase Brooke L Fridley Prabhakar Chalise Marylyn D Ritchie

Citations