TRM: a powerful two-stage machine learning approach for identifying SNP-SNP interactions.
TRM: a powerful two-stage machine learning approach for identifying SNP-SNP interactions.
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DOI：
10.1111/j.1469-1809.2011.00692.x
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发表时间：
2012-01
影响因子：
1.9
通讯作者：
Park JY
中科院分区：
文献类型：
--
作者：
Lin HY;Chen YA;Tsai YY;Qu X;Tseng TS;Park JY
研究表明，单核苷酸多态性（SNP）的相互作用可能对理解复杂疾病的病因起重要作用。机器学习方法为更有效地探索交互提供了有用的特性。我们提出了一种集成方法，结合了两种机器学习方法-随机森林（RF）和多元自适应回归样条(MARS) -来识别重要snp的子集并检测交互模式。在这种两阶段RF-MARS （TRM）方法中，RF首先用于检测snp的预测子集，然后MARS用于识别所选snp之间的相互作用模式。我们在四个模型中评估了TRM的性能：三个具有双向交互作用的因果模型和一个零模型。射频变量选择基于袋外分类错误率（OOB）和可变重要谱（IS）。首先，我们比较了RF和MARS的重要变量选择。我们的研究结果支持RFOOB在检测重要变量方面优于MARS和RFIS。我们还评估了识别TRM和MARS相互作用模式的真阳性率和假阳性率。该研究表明，TRMOOB （RFOOB + MARS）结合了RF和MARS的优势，可以在100个候选snp的情况下识别SNP-SNP相互作用模式。与MARS相比，TRMOOB具有更高的真阳性率和更低的假阳性率，特别是在搜索与结果有强烈关联的交互作用时。因此，在大规模遗传变异研究中，使用TRMOOB有利于探索SNP-SNP相互作用。
Studies have shown that interactions of single nucleotide polymorphism (SNP) may play an important role for understanding causes of complex disease. Machine learning approaches provide useful features to explore interactions more effectively and efficiently. We have proposed an integrated method that combines two machine learning methods - Random Forests (RF) and Multivariate Adaptive Regression Splines (MARS) - to identify a subset of important SNPs and detect interaction patterns. In this two-stage RF-MARS (TRM) approach, RF is first applied to detect a predictive subset of SNPs, and then MARS is used to identify the interaction patterns among the selected SNPs. We evaluated the TRM performances in four models: three causal models with one two-way interaction and one null model. RF variable selection was based on out-of-bag classification error rate (OOB) and variable important spectrum (IS). First, we compared the selection of important variable of RF and MARS. Our results support that RFOOB had better performance than MARS and RFIS in detecting important variables. We also evaluated the true positive rate and false positive rate of identifying interaction patterns in TRM and MARS. This study demonstrates that TRMOOB, which is RFOOB plus MARS, has combined the strengths of RF and MARS in identifying SNP-SNP interaction patterns in a scenario of 100 candidate SNPs. TRMOOB had greater true positive rate and lower false positive rate compared with MARS, particularly for searching interactions with a strong association with the outcome. Therefore the use of TRMOOB is favored for exploring SNP-SNP interactions in a large-scale genetic variation study.
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影响因子：
30.8
作者：
de Bakker, PIW;Yelensky, R;Altshuler, D
通讯作者：
Altshuler, D
影响因子：
1.8
作者：
Moore, JH
通讯作者：
Moore, JH
DOI：
10.1016/j.jsbmb.2009.10.015
发表时间：
2010-02-28
影响因子：
4.1
作者：
Ellem, Stuart J.;Risbridger, Gail P.
通讯作者：
Risbridger, Gail P.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
5.8
作者：
Nicodemus, Kristin K.;Malley, James D.
通讯作者：
Malley, James D.