Understanding the Evolutionary Process of Grammatical Evolution Neural Networks for Feature Selection in Genetic Epidemiology.
Understanding the Evolutionary Process of Grammatical Evolution Neural Networks for Feature Selection in Genetic Epidemiology.
复制标题
了解遗传流行病学中特征选择的语法进化神经网络的进化过程。
DOI：
10.1109/cibcb.2006.330945
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Ritchie,MarylynD
中科院分区：
文献类型：
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作者：
Motsinger,AlisonA;Reif,DavidM;Dudek,ScottM;Ritchie,MarylynD
识别预测复杂疾病的遗传因素/特征是人类遗传学的重要目标。常见疾病的潜在遗传结构中基因-基因相互作用的共性提出了一个艰巨的分析挑战。在此之前，我们介绍了一种语法进化神经网络（GENN）方法，该方法在没有任何边际主效应的情况下具有很高的检测此类相互作用的能力。虽然这种方法的成功是令人鼓舞的，但它也引出了关于算法本身的进化过程以及扩展该方法以解决具有大量特征的数据集的巨大维度的可行性的问题。当感兴趣的特征没有显示出主效应时，GENN如何能够建立正确的模型？根据特定数据集的规模，应该如何以及何时调整进化参数？在目前的研究中，我们监测性能的GENN在其进化过程中使用不同的人口规模和世代数。我们还比较了GENN的进化特征与随机搜索神经网络策略的进化特征，以更好地理解进化学习过程所提供的好处-包括染色体大小的优势以及两种方法生成的模型中功能与非功能特征的表示。最后，我们从GENN的特性中吸取教训，分析包含越来越多特征的数据集，以证明该方法的可扩展性
The identification of genetic factors/features that predict complex diseases is an important goal of human genetics. The commonality of gene-gene interactions in the underlying genetic architecture of common diseases presents a daunting analytical challenge. Previously, we introduced a grammatical evolution neural network (GENN) approach that has high power to detect such interactions in the absence of any marginal main effects. While the success of this method is encouraging, it elicits questions regarding the evolutionary process of the algorithm itself and the feasibility of scaling the method to account for the immense dimensionality of datasets with enormous numbers of features. When the features of interest show no main effects, how is GENN able to build correct models? How and when should evolutionary parameters be adjusted according to the scale of a particular dataset? In the current study, we monitor the performance of GENN during its evolutionary process using different population sizes and numbers of generations. We also compare the evolutionary characteristics of GENN to that of a random search neural network strategy to better understand the benefits provided by the evolutionary learning process - including advantages with respect to chromosome size and the representation of functional versus non-functional features within the models generated by the two approaches. Finally, we apply lessons from the characterization of GENN to analyses of datasets containing increasing numbers of features to demonstrate the scalability of the method