Related Experiment Video
Updated: Aug 5, 2025
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Feature impact assessment: a new score to identify relevant metabolomics features in artificial neural networks using
Danhui Wang1,2, Peyton Greenwood1, Matthias S Klein3
1Department of Food Science and Technology, The Ohio State University, Columbus, OH, 43210, USA.
Introduction:
Artificial Neural Networks (ANN) are increasingly used in metabolomics but are hard to interpret.
Objectives:
We aimed at developing a feature impact score that is model-agnostic, simple, and interpretable.
Methods:
Feature Impact Assessment (FIA) is calculated by varying combinations of features within their observed value range and checking for changes in prediction outcomes. FIA was implemented in R and tested on metabolomics datasets.
Results:
FIA exceeded LIME and SHAP in selecting biologically meaningful features. Values were comparable across different ANN architectures.
Conclusion:
FIA is a novel score ranking feature impact, helping interpreting ANN in the metabolomics field.
