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Open AccessArticle

G-Networks to Predict the Outcome of Sensing of Toxicity

1
University Côte d’Azur, I3S laboratory, UMR CNRS 7271, CS 40121, 06903 Sophia Antipolis CEDEX, France
2
Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3483; https://doi.org/10.3390/s18103483
Received: 14 August 2018 / Revised: 5 October 2018 / Accepted: 12 October 2018 / Published: 16 October 2018
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. View Full-Text
Keywords: G-networks; random neural network; chemical compounds; machine learning; toxicity G-networks; random neural network; chemical compounds; machine learning; toxicity
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Grenet, I.; Yin, Y.; Comet, J.-P. G-Networks to Predict the Outcome of Sensing of Toxicity. Sensors 2018, 18, 3483.

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