Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.
Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.
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DOI：
10.1186/1471-2105-4-28
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发表时间：
2003-07-07
影响因子：
3
通讯作者：
Moore JH
中科院分区：
文献类型：
--
作者：
Ritchie MD;White BC;Parker JS;Hahn LW;Moore JH
在数据分析之前，适当地定义神经网络结构对于成功的数据挖掘至关重要。当数据的基础模型未知时，这可能具有挑战性。本研究的目的是确定使用遗传编程作为机器学习策略优化神经网络架构是否会提高神经网络在常见人类疾病研究中建模和检测基因间非线性相互作用的能力。使用模拟数据，我们表明，遗传编程优化的神经网络方法是能够模拟基因-基因的相互作用，以及传统的反向传播神经网络。此外，遗传编程优化的神经网络是优于传统的反向传播神经网络方法的预测能力和功率检测基因间的相互作用时，非功能性多态性的存在。这项研究表明，用于优化神经网络架构的机器学习策略可能优于传统的试错法，用于识别和表征常见复杂人类疾病中的基因-基因相互作用。
Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.
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影响因子：
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作者：
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通讯作者：
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