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Artificial Neural Network Comparison on hERG Channel Blockade Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 14 |
| Year of Publication: 2022 |
| Authors: Haibo Liu, Tessa De Korte, Sylvain Bernasconi, Christophe Bleunven, Damiano Lombardi, Muriel Boulakia |
10.5120/ijca2022922119
|
Haibo Liu, Tessa De Korte, Sylvain Bernasconi, Christophe Bleunven, Damiano Lombardi, Muriel Boulakia . Artificial Neural Network Comparison on hERG Channel Blockade Detection. International Journal of Computer Applications. 184, 14 ( May 2022), 1-9. DOI=10.5120/ijca2022922119
Abstract
This work will present a comparison of several Artificial Neural Network methods for a classification problem related to cardiac safety assessment. Given the extracellular field potential recorded by means of micro-electrode arrays, the aim is to determine whether a given chemical drug is altering the electrical activity of cardiomyocytes by disrupting the normal behavior of the hERG channels. To do so, this work has considered four different Neural Network methods and compared them in terms of accuracy and computational costs. The conclusion is that, among the tested architectures, the Multilayer Perceptron (MLP) and multivariate 1-dimensional Convolutional Neural Network (1D-CNN) give the most promising results.
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