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- W4297235583 endingPage "11269" @default.
- W4297235583 startingPage "11269" @default.
- W4297235583 abstract "Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics–AI systems, limitations thereof and recent tools were also discussed." @default.
- W4297235583 created "2022-09-28" @default.
- W4297235583 creator A5021119674 @default.
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- W4297235583 creator A5089714768 @default.
- W4297235583 creator A5091005090 @default.
- W4297235583 date "2022-09-24" @default.
- W4297235583 modified "2023-10-18" @default.
- W4297235583 title "Precision Medicine Approaches with Metabolomics and Artificial Intelligence" @default.
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