Abstract
In the last few years, DNA microarray technology has attained a very important role in biological and biomedical research. It enables analyzing the relations among thousands of genes simultaneously, generating huge amounts of data. The gene regulatory networks represent, in a graph data structure, genes or gene products and the functional relationships between them. These models have been fully used in Bioinformatics because they provide an easy way to understand gene expression regulation.
Nowadays, a lot of gene regulatory network algorithms have been developed as knowledge extraction techniques. A very important task in all these studies is to assure the network models reliability in order to prove that the methods used are precise. This validation process can be carried out by using the inherent information of the input data or by using external biological knowledge. In this last case, these sources of information provide a great opportunity of verifying the biological soundness of the generated networks.
In this work, authors present a gene regulatory network validation framework. The proposed approach consists in identifying the biological knowledge included in the input network. To do that, the biochemical pathways information stored in KEGG database will be used.
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Díaz-Díaz, N., Gómez-Vela, F., Rodriguez-Baena, D.S., Aguilar-Ruiz, J. (2011). Gene Regulatory Networks Validation Framework Based in KEGG. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_34
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DOI: https://doi.org/10.1007/978-3-642-21222-2_34
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