Artificial Neural Network Comparison on hERG Channel Blockade Detection
International Journal of Computer Applications, vol. 184, pp. 1–10
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.
Authors 5
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Tessa de Korte Aachen
Institut national de recherche en sciences et technologies du numérique · Inserm · Leiden University Medical Center · Laboratoire de Mathématiques de Versailles
Affiliation as printed
Leiden University Medical Center 2333 ZA Leiden , Netherlands
Inria
Leiden University Medical Center 2333 ZA Leiden, Netherlands
NOTOCORD part of Instem 78230 Le Pecq, France
Research Scientist at Inria Inria, 751012, Paris, France
professor at Laboratoire de Mathematiques de Versailles 78000, Versailles, France
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Sylvain Bernasconi Aachen
Institut national de recherche en sciences et technologies du numérique · Inserm · Leiden University Medical Center · Laboratoire de Mathématiques de Versailles
Affiliation as printed
NOTOCORD part of Instem 78230 Le Pecq , France
Inria
Leiden University Medical Center 2333 ZA Leiden, Netherlands
NOTOCORD part of Instem 78230 Le Pecq, France
Research Scientist at Inria Inria, 751012, Paris, France
professor at Laboratoire de Mathematiques de Versailles 78000, Versailles, France
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Christophe Bleunven Aachen
Institut national de recherche en sciences et technologies du numérique · Inserm · Leiden University Medical Center · Laboratoire de Mathématiques de Versailles
Affiliation as printed
NOTOCORD part of Instem 78230 Le Pecq , France
Inria
Leiden University Medical Center 2333 ZA Leiden, Netherlands
NOTOCORD part of Instem 78230 Le Pecq, France
Research Scientist at Inria Inria, 751012, Paris, France
professor at Laboratoire de Mathematiques de Versailles 78000, Versailles, France
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Damiano Lombardi Aachen
Institut national de recherche en sciences et technologies du numérique · Inserm · Leiden University Medical Center · Laboratoire de Mathématiques de Versailles
Affiliation as printed
Research Scientist at Inria Inria , 751012 , Paris , France
Inria
Leiden University Medical Center 2333 ZA Leiden, Netherlands
NOTOCORD part of Instem 78230 Le Pecq, France
Research Scientist at Inria Inria, 751012, Paris, France
professor at Laboratoire de Mathematiques de Versailles 78000, Versailles, France
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Muriel Boulakia Aachen
Institut national de recherche en sciences et technologies du numérique · Inserm · Leiden University Medical Center · Laboratoire de Mathématiques de Versailles
Affiliation as printed
professor at Laboratoire de Mathematiques de Versailles 78000 , Versailles , France
Inria
Leiden University Medical Center 2333 ZA Leiden, Netherlands
NOTOCORD part of Instem 78230 Le Pecq, France
Research Scientist at Inria Inria, 751012, Paris, France
professor at Laboratoire de Mathematiques de Versailles 78000, Versailles, France
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