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.
Highlights
G-Networks [1] are a family of queueing networks with a convenient and computationally efficient product form mathematical solution
A recent application of G-Networks is to the modelling of systems which operate with intermittent sources of energy, known as Energy Packet Networks [10,11,12,13,14,15]
The Multi Layer RNN (MLRNN), Random Neural Network (RNN) and XGBoost algorithms are exploited to classify the 50 × 37 pairs of training and testing datasets and results are summarized into Figure 1
Summary
G-Networks [1] are a family of queueing networks with a convenient and computationally efficient product form mathematical solution. G-Networks incorporate useful primitives, such as the transfer of jobs between servers or the removal of batches of jobs from excessively busy servers, which were developed in several successive papers including [3,4,5,6] They have a wealth of diverse applications as a tool to analyse and optimise the effects of dynamic load balancing in large scale networks and distributed computer systems [7]. The RNN has been used for modelling natural neuronal networks [21], and for protein alignment [22] It has been used with its learning algorithm [18] in several image processing applications including learning colour textures [23], the accurate evaluation of tumours from brain.
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