G-Networks to Predict the Outcome of Sensing of Toxicity

被引：3

作者：

Grenet, Ingrid ^{[1
]}

Yin, Yonghua ^{[2
]}

Comet, Jean-Paul ^{[1
]}

机构：

[1] Univ Cote dAzur, Lab I3S, CNRS, UMR 7271, CS 40121, F-06903 Sophia Antipolis, France

[2] Imperial Coll, Intelligent Syst & Networks Grp, Dept Elect & Elect Engn, London SW7 2AZ, England

来源：

SENSORS | 2018年 / 18卷 / 10期

关键词：

G-networks; random neural network; chemical compounds; machine learning; toxicity; FUNCTION APPROXIMATION; NEURAL-NETWORKS; VIDEO QUALITY;

D O I：

10.3390/s18103483

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：10