Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods
Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods
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DOI：
10.1371/journal.pone.0040916
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发表时间：
2012-07-24
期刊：
影响因子：
3.7
通讯作者：
Beyer, Andreas
中科院分区：
文献类型：
--
作者：
Ackermann, Marit;Clement-Ziza, Mathieu;Beyer, Andreas
表达数量性状位点（eQTL）定位是一种广泛应用于揭示基因间调控关系的技术。已经开发了一系列方法来绘制表达性状和基因型之间的联系。DREAM（逆向工程评估与方法对话）计划是一个社区项目，旨在客观地评估解决特定系统生物学问题的不同计算方法的相对性能。DREAM5挑战的目标之一是从合成的遗传变异和基因表达数据中反向工程遗传相互作用网络，模拟eQTL定位问题。在这个框架中，我们提出了一种方法，其独创性在于使用现有机器学习算法的组合（委员会）。虽然它不是最好的表现，但这种方法是迄今为止最精确的平均。比赛结束后，我们继续沿着这个方向，利用DREAM5的数据评估其他委员会，并开发了一种依赖随机森林和LASSO的方法。它以略低的平均灵敏度为代价，实现了比DREAM最佳性能高得多的平均精度。
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.