Abstract
Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. The interest shown over network models and systems biology is rapidly raising. Genetic networks arise as an essential task to mine these data since they explain the function of genes in terms of how they influence other genes. Many modeling approaches have been proposed for building genetic networks up. However, it is not clear what the advantages and disadvantages of each model are. There are several ways to discriminate network building models, being one of the most important whether the data being mined presents a static or dynamic fashion. In this work we compare static and dynamic models over a problem related to the inflammation and the host response to injury. We show how both models provide complementary information and cross-validate the obtained results.
Keywords
- Boolean Function
- Genetic Network
- Boolean Network
- Boolean Model
- KEGG Database
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Rubio-Escudero, C., Harari, O., Cordón, O., Zwir, I. (2007). Modeling Genetic Networks: Comparison of Static and Dynamic Models. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_8
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DOI: https://doi.org/10.1007/978-3-540-71783-6_8
Publisher Name: Springer, Berlin, Heidelberg
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