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Feed-forward neural network model for hunger and satiety related VAS score prediction

BACKGROUND: An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding. METHODS: A multilayer feed-forward neural network was...

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Autores principales: Krishnan, Shaji, Hendriks, Henk F. J., Hartvigsen, Merete L., de Graaf, Albert A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4936290/
https://www.ncbi.nlm.nih.gov/pubmed/27387922
http://dx.doi.org/10.1186/s12976-016-0043-4
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author Krishnan, Shaji
Hendriks, Henk F. J.
Hartvigsen, Merete L.
de Graaf, Albert A.
author_facet Krishnan, Shaji
Hendriks, Henk F. J.
Hartvigsen, Merete L.
de Graaf, Albert A.
author_sort Krishnan, Shaji
collection PubMed
description BACKGROUND: An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding. METHODS: A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions. RESULTS: The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms. CONCLUSIONS: From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.
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spelling pubmed-49362902016-07-07 Feed-forward neural network model for hunger and satiety related VAS score prediction Krishnan, Shaji Hendriks, Henk F. J. Hartvigsen, Merete L. de Graaf, Albert A. Theor Biol Med Model Research BACKGROUND: An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding. METHODS: A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions. RESULTS: The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms. CONCLUSIONS: From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available. BioMed Central 2016-07-07 /pmc/articles/PMC4936290/ /pubmed/27387922 http://dx.doi.org/10.1186/s12976-016-0043-4 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Krishnan, Shaji
Hendriks, Henk F. J.
Hartvigsen, Merete L.
de Graaf, Albert A.
Feed-forward neural network model for hunger and satiety related VAS score prediction
title Feed-forward neural network model for hunger and satiety related VAS score prediction
title_full Feed-forward neural network model for hunger and satiety related VAS score prediction
title_fullStr Feed-forward neural network model for hunger and satiety related VAS score prediction
title_full_unstemmed Feed-forward neural network model for hunger and satiety related VAS score prediction
title_short Feed-forward neural network model for hunger and satiety related VAS score prediction
title_sort feed-forward neural network model for hunger and satiety related vas score prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4936290/
https://www.ncbi.nlm.nih.gov/pubmed/27387922
http://dx.doi.org/10.1186/s12976-016-0043-4
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