Journal
ANALYTICAL CHEMISTRY
Volume 95, Issue 13, Pages 5542-5552Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.2c04402
Keywords
-
Categories
Ask authors/readers for more resources
Multiclass metabolomics is widely used in clinical practice for understanding disease progression and identifying diagnostic biomarkers. It is more challenging than the binary problem due to the complexity of determining class decision boundaries. However, there is still a lack of a systematic assessment for selecting appropriate methods in multiclass metabolomics.
Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available