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

Emily R. Holzinger, Scott M. Dudek, Alex T. Frase, Brooke Fridley, Prabhakar Chalise, Marylyn D. Ritchie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Recent technological innovations have catalyzed 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.

Original languageEnglish (US)
Title of host publicationEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 10th European Conference, EvoBIO 2012, Proceedings
Pages134-143
Number of pages10
DOIs
StatePublished - Apr 3 2012
Event10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012 - Malaga, Spain
Duration: Apr 11 2012Apr 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7246 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012
CountrySpain
CityMalaga
Period4/11/124/13/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Holzinger, E. R., Dudek, S. M., Frase, A. T., Fridley, B., Chalise, P., & Ritchie, M. D. (2012). Comparison of methods for meta-dimensional data analysis using in silico and biological data sets. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 10th European Conference, EvoBIO 2012, Proceedings (pp. 134-143). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7246 LNCS). https://doi.org/10.1007/978-3-642-29066-4_12