Article

Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification

2022-09-19

Abstract excerpt

Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants...

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Literature Corpus work: 38eea55e-e7b9-5856-bb7e-e4f1503899e0
DOI: 10.1101/2022.09.19.508550

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