Towards Optimizing Neural Network-Based Quantification for NMR Metabolomics.

ABSTRACT: Background: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz ¹H-NMR spectra of comple

SUBMITTER: Johnson H 

PROVIDER: S-EPMC12029129 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature