A new version of this entry is available:
Loading...
Article
2025
Nutrient–response modeling with a single and interpretable artificial neuron
Nutrient–response modeling with a single and interpretable artificial neuron
Abstract (English)
Precise estimation of nutrient requirements and utilization efficiency is fundamental to nutritional sciences, yet it is mainly performed using classical nonlinear regression models. These models are interpretable but require careful selection of the functional form and initial parameter values. Flexible machine learning (ML) methods are seemingly disliked due to their perceived “black box” nature, which can obscure biological insight. A minimal and interpretable ML framework addresses this gap in nutrient–response modeling. The proposed approach uses a single artificial neuron with a hyperbolic tangent activation. Mathematically, this resembles a four-parameter sigmoidal function but with greater flexibility and distinct parameter definitions, allowing capture of the monotonic, saturating dynamics typical of essential nutrient responses. The method is enhanced with modern ML best practices, including data augmentation, Bayesian regularization, and bootstrap resampling, providing robust, uncertainty-quantified estimates of key nutritional metrics—such as asymptotic response, inflection point, and nutrient requirements—even from small datasets. Evaluations across 12 diverse datasets from poultry and fish studies, including amino acids and phosphorus, demonstrated that the single artificial neuron matches or exceeds the performance of classical models while providing full analytical transparency. The framework is implemented as a no-code graphical application, ‘NutriCurvist’, offering an easy-to-use alternative tool for nutrient-response modeling to support data-driven, precision nutrition.
File is subject to an embargo until
This is a correction to:
A correction to this entry is available:
This is a new version of:
Other version
Notes
Publication license
Publication series
Published in
Scientific reports, 15 (2025), 41836.
https://doi.org/10.1038/s41598-025-29267-w.
ISSN: 2045-2322
London : Nature Publishing Group UK
Other version
Faculty
Institute
Examination date
Supervisor
Cite this publication
Rodehutscord, M., & Ahmadi, H. (2025). Nutrient–response modeling with a single and interpretable artificial neuron. Scientific reports, 15. https://doi.org/10.1038/s41598-025-29267-w
Edition / version
Citation
DOI
ISSN
ISBN
Language
English
Publisher
Publisher place
Classification (DDC)
610 Medicine and health
Original object
University bibliography
Standardized keywords (GND)
Sustainable Development Goals
BibTeX
@article{Rodehutscord2025,
doi = {10.1038/s41598-025-29267-w},
author = {Rodehutscord, Markus},
title = {Nutrient–response modeling with a single and interpretable artificial neuron},
journal = {Scientific Reports},
year = {2025},
volume = {15},
}