Data Mining and Computationally Intensive Methods: Summary of Group 7 Contributions to Genetic Analysis Workshop 13

Tracy J. Costello, Catherine T. Falk, Qian K. Ye

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The Framingham Heart Study data, as well as a related simulated data set, were generously provided to the participants of the Genetic Analysis Workshop 13 in order that newly developed and emerging statistical methodologies could be tested on that well-characterized data set. The impetus driving the development of novel methods is to elucidate the contributions of genes, environment, and interactions between and among them, as well as to allow comparison between and validation of methods. The seven papers that comprise this group used data-mining methodologies (tree-based methods, neural networks, discriminant analysis, and Bayesian variable selection) in an attempt to identify the underlying genetics of cardiovascular disease and related traits in the presence of environmental and genetic covariates. Data-mining strategies are gaining popularity because they are extremely flexible and may have greater efficiency and potential in identifying the factors involved in complex disorders. While the methods grouped together here constitute a diverse collection, some papers asked similar questions with very different methods, while others used the same underlying methodology to ask very different questions. This paper briefly describes the data-mining methodologies applied to the Genetic Analysis Workshop 13 data sets and the results of those investigations.

Original languageEnglish (US)
JournalGenetic Epidemiology
Volume25
Issue numberSUPPL. 1
DOIs
StatePublished - 2003
Externally publishedYes

Fingerprint

Data Mining
Education
Gene-Environment Interaction
Inborn Genetic Diseases
Discriminant Analysis
Cardiovascular Diseases
Datasets

Keywords

  • Association test
  • Cardiovascular disease
  • Discriminant analysis
  • Framingham Heart Study
  • Genetic linkage
  • Glucose levels
  • Hypertension
  • Neural networks
  • Random forests
  • Stochastic search variable selection
  • Systolic blood pressure
  • Tree-based methods

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Data Mining and Computationally Intensive Methods : Summary of Group 7 Contributions to Genetic Analysis Workshop 13. / Costello, Tracy J.; Falk, Catherine T.; Ye, Qian K.

In: Genetic Epidemiology, Vol. 25, No. SUPPL. 1, 2003.

Research output: Contribution to journalArticle

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