Abstract
Machine learning is a form of artificial intelligence (AI) that provides computers with the ability to learn generally without being explicitly programmed. Machine learning refers to the ability of computer programs to adapt when exposed to new data. Here we examine the use of machine learning for use with untargeted metabolomics data, when it is appropriate to use, and questions it can answer. We provide an example workflow for training and testing a simple binary classifier, a multiclass classifier and a support vector machine using the Waikato Environment for Knowledge Analysis (Weka), a toolkit for machine learning. This workflow should provide a framework for greater integration of machine learning with metabolomics study.
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Acknowledgments
The authors would also like to acknowledge that this work was part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy.
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1 Electronic Supplementary Material
Supplementary File 1
Example data files containing mass spectrometry based intensity (relative abundance) information for metabolites in both .csv and .arff format (ZIP 524 kb)
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Heinemann, J. (2019). Machine Learning in Untargeted Metabolomics Experiments. In: Baidoo, E. (eds) Microbial Metabolomics. Methods in Molecular Biology, vol 1859. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8757-3_17
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DOI: https://doi.org/10.1007/978-1-4939-8757-3_17
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