Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks. We use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis. The experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.

中文翻译：

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一种利用仿生优化神经网络的嵌入式基因选择方法。

基因选择是指从基因表达谱中找到一小部分判别基因。如何选择有效影响特定表型特征的基因是生物学领域的重要研究工作。神经网络在处理非线性数据时具有更好的拟合能力，并且可以自动，灵活地捕获特征。在这项工作中，我们提出了一种使用神经网络的嵌入式基因选择方法。训练结束后，通过计算权重系数可以获得重要的基因。为了解决神经网络的黑匣子问题，使训练结果在神经网络中具有可解释性，我们采用仿生的思想构建原始特征基因的仿生特征基因。这种方法不仅使每个特征基因相互竞争，而且还使每个特征基因与其敲除特征基因竞争。这种方法可以帮助选择影响神经网络决策的关键基因。我们使用玉米类胡萝卜素，生育酚甲基转移酶，棉子糖家族低聚糖和人类乳腺癌数据集进行验证和分析。实验结果表明，仿生优化神经网络方法具有比其他现有算法更好的检测效果，尤其适用于处理非线性基因表达和表型数据。棉子糖家族的低聚糖和人类乳腺癌的数据集做验证和分析。实验结果表明，仿生优化神经网络方法具有比其他现有算法更好的检测效果，尤其适用于处理非线性基因表达和表型数据。棉子糖家族的低聚糖和人类乳腺癌的数据集做验证和分析。实验结果表明，仿生优化神经网络方法具有比其他现有算法更好的检测效果，尤其适用于处理非线性基因表达和表型数据。