Feature impact assessment: a new score to identify relevant metabolomics features in artificial neural networks using validated labels.

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Abstract

Artificial Neural Networks (ANN) are increasingly used in metabolomics but are hard to interpret.We aimed at developing a feature impact score that is model-agnostic, simple, and interpretable.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.FIA exceeded LIME and SHAP in selecting biologically meaningful features. Values were comparable across different ANN architectures.FIA is a novel score ranking feature impact, helping interpreting ANN in the metabolomics field.© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.