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Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods

Author

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  • Marit Ackermann
  • Mathieu Clément-Ziza
  • Jacob J Michaelson
  • Andreas Beyer

Abstract

Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.

Suggested Citation

  • Marit Ackermann & Mathieu Clément-Ziza & Jacob J Michaelson & Andreas Beyer, 2012. "Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-8, July.
    • Handle: RePEc:plo:pone00:0040916
    DOI: 10.1371/journal.pone.0040916
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    References listed on IDEAS

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    1. Karl W. Broman & Terence P. Speed, 2002. "A model selection approach for the identification of quantitative trait loci in experimental crosses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 641-656, October.
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    1. Nikola Simidjievski & Ljupčo Todorovski & Sašo Džeroski, 2016. "Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-27, April.

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