Background: Gene regulation represents a very complex mechanism in the cell initiated to increase or decrease gene expression. This regulation of genes forms a Gene regulatory Network GRN composed of a collection of genes and products of genes in interaction. The high throughput technologies that generate a huge volume of gene expression data are useful for analyzing the GRN. The biologists are interested in the relevant genetic knowledge hidden in these data sources. Although, the knowledge extracted by the different data mining approaches of the literature is insufficient for inferring the GRN topology or does not give a good representation of the real genetic regulation in the cell.

Objective: In this work, we performed the extraction of genetic interactions from the high throughput technologies, such as the microarrays or DNA chips.

Methods: In this paper, in order to extract expressive and explicit knowledge about the interactions between genes, we used the method of gradual patterns and rules extraction applied on numerical data that extracts the frequent co-variations between gene expression values. Furthermore, we choose to integrate experimental biological data and biological knowledge in the process of knowledge extraction of genetic interactions.

Results: The validation results on real gene expression data of the model plant Arabidopsis and human lung cancer showed the performance of this approach.

Conclusion: The extracted gradual rules express the genetic interactions composed of a GRN. These rules help to understand complex systems and cellular functions.

背景：基因调控代表着细胞中增加或减少基因表达的非常复杂的机制。基因的这种调节形成了基因调节网络GRN，该网络由相互作用的基因和基因产物的集合组成。产生大量基因表达数据的高通量技术可用于分析GRN。生物学家对隐藏在这些数据源中的相关遗传知识感兴趣。虽然，通过文献的不同数据挖掘方法提取的知识不足以推断GRN拓扑，或者不能很好地表示细胞中的实际遗传调控。

目的：在这项工作中，我们从高通量技术（如微阵列或DNA芯片）中提取了遗传相互作用。

方法：在本文中，为了提取有关基因之间相互作用的表达性和显性知识，我们使用应用于数字数据的渐进模式和规则提取方法来提取基因表达值之间的频繁协变量。此外，我们选择在遗传相互作用的知识提取过程中整合实验生物学数据和生物学知识。

结果：对模型植物拟南芥和人肺癌的真实基因表达数据的验证结果表明了该方法的有效性。

结论：提取的渐进规则表达了由GRN组成的遗传相互作用。这些规则有助于了解复杂的系统和蜂窝功能。