G-Networks to Predict the Outcome of Sensing of Toxicity.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through the prediction of their bioactivity from the compounds' physico-chemical structure, and propose that it be automated using machine learning (ML) techniques. Specifically the Random Neural Network is shown to be an effective analytical tool to this effect, and the approach is illustrated and compared with several ML techniques.

Authors

  • Ingrid Grenet
    University Côte d'Azur, I3S laboratory, UMR CNRS 7271, CS 40121, 06903 Sophia Antipolis CEDEX, France. grenet@i3s.unice.fr.
  • Yonghua Yin
    Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK. y.yin14@imperial.ac.uk.
  • Jean-Paul Comet
    University Côte d'Azur, I3S laboratory, UMR CNRS 7271, CS 40121, 06903 Sophia Antipolis CEDEX, France. Jean-Paul.Comet@univ-cotedazur.fr.