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.
Keywords: Knowledge extraction, gene expression data, gradual rules, GRN, genetic interaction, arabidopsis, floral
development, lung cancer.
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