2023
DOI: 10.3390/metabo13010097
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Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data

Abstract: Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph perfo… Show more

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Cited by 2 publications
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