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International Work-Conference on Artificial Neural Networks

IWANN 1999: Engineering Applications of Bio-Inspired Artificial Neural Networks pp 497–505Cite as

Variable selection by recurrent neural networks. Application in structure activity relationship study of cephalosporins

  • Engeneering Applications
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

Abstract

Two methods for variable selection which are efficiently implemented by Hopfield-like Neural networks are described. Qualitative SAR models using the selected variables by both neural networks variable selection methods are built. The biological activity against Staphylococcus aureus of cephalosporins was used as dependent variable. The final correlation between observed and predicted activity values are good, indicating that the informative weight of the favored variables is high, providing a sound basis to select a good variable set of in qualitative structure-activity relationships (SAR) modeling.

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Author information

Authors and Affiliations

  1. University of Antioquia, Medellin, Colombia

    Nancy Lopez

  2. Institute of Cybernetics Mathematics and Physics, La Habana, Cuba

    Nancy Lopez

  3. Institute of Nuclear Sciences and Technology, La Habana, Cuba

    Roberto Cruz

  4. Center of Pharmaceutical Chemistry, La Habana, Cuba

    Belsis LLorente

Authors
  1. Nancy Lopez
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  2. Roberto Cruz
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  3. Belsis LLorente
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Editor information

José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Lopez, N., Cruz, R., LLorente, B. (1999). Variable selection by recurrent neural networks. Application in structure activity relationship study of cephalosporins. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100517

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  • DOI: https://doi.org/10.1007/BFb0100517

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

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