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Evolutionary Fuzzy Modelling for Drug Resistant HIV-1 Treatment Optimization

  • Chapter
Engineering Evolutionary Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 82))

Summary

Fuzzy relational models for genotypic drug resistance analysis in Human Immunodeficiency Virus type 1 (HIV-1) are discussed. Fuzzy logic is introduced to model high-level medical language, viral and pharmacological dynamics. In-vitro experiments of genotype/phenotype pairs and in-vivo clinical data bases are the base for the knowledge mining. Fuzzy evolutionary algorithms and fuzzy evaluation functions are proposed to mine resistance rules, to improve computational performances and to select relevant features.

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Authors and Affiliations

  1. Faculty of Computer Science Engineering Department of Computer Science and Automation, University of Roma TRE, Italy

    Mattia Prosperi & Giovanni Ulivi

Authors
  1. Mattia Prosperi
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  2. Giovanni Ulivi
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Editors and Affiliations

  1. Centre for Quantifiable Quality of Service in Communication Systems (Q2S), Centre of Excellence Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  2. Department of Computer Science Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Kogalniceanu 1, 400084, Cluj - Napoca, Romania

    Crina Grosan

  3. Department of Electrical & Computer Engineering, University of Alberta, ECERF Bldg., 2nd floor, Edmonton, AB, T6G 2V4, Canada

    Witold Pedrycz

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Prosperi, M., Ulivi, G. (2008). Evolutionary Fuzzy Modelling for Drug Resistant HIV-1 Treatment Optimization. In: Abraham, A., Grosan, C., Pedrycz, W. (eds) Engineering Evolutionary Intelligent Systems. Studies in Computational Intelligence, vol 82. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75396-4_9

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  • DOI: https://doi.org/10.1007/978-3-540-75396-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75395-7

  • Online ISBN: 978-3-540-75396-4

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