Penalized logistic regression for detecting gene interactions
Penalized logistic regression for detecting gene interactions
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DOI：
10.1093/biostatistics/kxm010
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发表时间：
2008-01-01
期刊：
影响因子：
2.1
通讯作者：
Hastie, Trevor
中科院分区：
文献类型：
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作者：
Park, Mee Young;Hastie, Trevor
我们建议使用一种具有L-2正则化的逻辑回归（LR）来拟合基因-基因和基因环境相互作用模型。研究表明，许多常见疾病都受到某些基因相互作用的影响。具有二次惩罚的LR模型不仅正确地表征了有影响力的基因沿着与它们的相互作用结构，而且在处理具有二进制响应的高维离散因子时产生额外的益处。我们说明了使用L-2正则化方案的优点，并将其性能与“多因子降维”和“FlexTree”（2个最近用于识别基因-基因相互作用的工具）进行比较。通过模拟和真实的数据集，我们证明了我们的方法优于其他方法在相互作用结构的识别以及预测精度。此外，我们通过自助分析验证了所选因素的显著性。
We propose using a variant of logistic regression (LR) with L-2-regularization to fit gene-gene and gene environment interaction models. Studies have shown that many common diseases are influenced by interaction of certain genes. LR models with quadratic penalization not only correctly characterizes the influential genes along with their interaction structures but also yields additional benefits in handling high-dimensional, discrete factors with a binary response. We illustrate the advantages of using an L-2-regularization scheme and compare its performance with that of "multifactor dimensionality reduction" and "FlexTree," 2 recent tools for identifying gene-gene interactions. Through simulated and real data sets, we demonstrate that our method outperforms other methods in the identification of the interaction structures as well as prediction accuracy. In addition, we validate the significance of the factors selected through bootstrap analyses.