In systems biology, it is challenging to accurately infer a regulatory network from time-series gene expression data, and a variety of methods have been proposed. Most of them were computationally inefficient in inferring very large networks, though, because of the increasing number of candidate regulatory genes. Although a recent approach called GABNI (genetic algorithm-based Boolean network inference) was presented to resolve this problem using a genetic algorithm, there is room for performance improvement because it employed a limited representation model of regulatory functions.

中文翻译：

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从时序基因表达推断布尔网络的神经进化方法

在系统生物学中，从时序基因表达数据准确推断调控网络具有挑战性，并且已经提出了多种方法。但是，由于候选调控基因的数量不断增加，它们中的大多数在推断非常大的网络方面效率低下。尽管提出了一种称为GABNI（基于遗传算法的布尔网络推理）的最新方法来使用遗传算法解决此问题，但是由于该方法采用了有限的调节功能表示模型，因此仍有改进空间。