Abstract
Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific computational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1 score of 0.8180 and a Matthews correlation coefficient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.
Document Type
Article
Publication Date
11-2023
Digital Object Identifier (DOI)
https://doi.org/10.3390/metabo13111120
Funding Information
The research was funded by the National Science Foundation, grant number: 2020026 (PI Moseley), and by the National Institutes of Health, grant number: P42 ES007380 (University of Kentucky Super-fund Research Program Grant; PI Pennell). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation nor the National Institute of Environmental Health Sciences.
Repository Citation
Huckvale, Erik D.; Powell, Christian D.; Jin, Huan; and Moseley, Hunter N. B., "Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites" (2023). Markey Cancer Center Faculty Publications. 303.
https://uknowledge.uky.edu/markey_facpub/303
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Notes/Citation Information
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).