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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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