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Evolving a Fuzzy Rulebase to Model Gene Expression

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Book cover Fuzzy Systems in Bioinformatics and Computational Biology

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 242))

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Summary

This chapter describes the use of genetic programming to evolve a fuzzy rule base to model gene expression. We describe the problem of genetic regulation in details and offer some reasons as to why many computational methods have difficulties in modeling it. We describe how a fuzzy rule base can be applied to this problem as well as how genetic programming can be used to evolve a fuzzy rule base to extract explanatory rules from microarray data obtained in the real experiments, which give us data sets that have thousands of features, but only a limited number of measurements in time. The algorithm allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes that are already known.

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Author information

Authors and Affiliations

  1. Faculdade Salesiana Maria Auxiliadora, CEPEL,  

    Ricardo Linden

  2. COPPE-UFRJ,  

    Amit Bhaya

Authors
  1. Ricardo Linden
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  2. Amit Bhaya
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Editor information

Editors and Affiliations

  1. Honda Research Institute Honda Research Institute Europe , Offenbach, Germany

    Yaochu Jin

  2. Nanyang Technological University, Singapore

    Lipo Wang

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© 2009 Springer-Verlag Berlin Heidelberg

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Linden, R., Bhaya, A. (2009). Evolving a Fuzzy Rulebase to Model Gene Expression. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_10

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  • DOI: https://doi.org/10.1007/978-3-540-89968-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89967-9

  • Online ISBN: 978-3-540-89968-6

  • eBook Packages: EngineeringEngineering (R0)

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