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Teamwork: improved eQTL mapping using combinations of machine learning methods
Journal article   Open access   Peer reviewed

Teamwork: improved eQTL mapping using combinations of machine learning methods

Marit Ackermann, Mathieu Clément-Ziza, Jacob J Michaelson and Andreas Beyer
PloS one, Vol.7(7), pp.e40916-e40916
2012
DOI: 10.1371/journal.pone.0040916
PMCID: PMC3404069
PMID: 22911718
url
https://doi.org/10.1371/journal.pone.0040916View
Published (Version of record) Open Access

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.
 Computational Biology - methods Algorithms Artificial Intelligence Gene Expression Profiling - methods Genotype ROC Curve Chromosome Mapping Gene Regulatory Networks Quantitative Trait Loci

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