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A genetic algorithm for simulating correlated binary data from biomedical research.

Jochen Kruppa1, Bernd Lepenies2, Klaus Jung3

  • 1Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Hannover, Germany.

Computers in Biology and Medicine
|November 14, 2017
PubMed
Summary
This summary is machine-generated.

A new genetic algorithm effectively simulates correlated binary data for biomedical research, addressing limitations of existing methods for correlation structures and high-dimensional data analysis. This approach enhances simulation capabilities for robust statistical evaluations.

Keywords:
Computer simulationCorrelated binary dataGenetic algorithmHigh-dimensional dataRandom number generation

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Bioinformatics

Background:

  • Correlated binary data are prevalent in biomedical research, necessitating accurate simulation methods for analysis evaluation.
  • Existing simulation techniques often lack the flexibility to cover diverse correlation structures or are not readily available as software.
  • High-dimensional data present unique challenges for simulating correlated binary outcomes.

Purpose of the Study:

  • To introduce a novel genetic algorithm for simulating correlated binary data with specified marginal distributions and correlation structures.
  • To evaluate the algorithm's performance in terms of speed, precision, and suitability for high-dimensional datasets.
  • To demonstrate the algorithm's practical application in analyzing high-throughput glycan array data.

Main Methods:

  • A genetic algorithm was developed to generate large representative matrices approximating desired correlation structures.
  • The algorithm was evaluated against two existing methods under various marginal frequencies and correlation scenarios.
  • The approach was tested for its efficacy in generating high-dimensional correlated binary data.

Main Results:

  • The proposed genetic algorithm successfully approaches desired correlation structures under given marginal distributions.
  • Performance evaluation demonstrated the algorithm's speed, precision, and capability for high-dimensional data generation.
  • The method proved useful in simulating the power of global test procedures, as shown with glycan array data.

Conclusions:

  • The genetic algorithm offers a flexible and effective method for simulating correlated binary data, overcoming limitations of existing approaches.
  • The algorithm is suitable for both low- and high-dimensional data and various correlation structures.
  • The implementation is available within the R-package 'RepeatedHighDim', facilitating its use in biomedical research.