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1.
Int J Mol Sci ; 23(19)2022 Sep 24.
Article in English | MEDLINE | ID: mdl-36232571

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.


Subject(s)
Artificial Intelligence , Precision Medicine , Algorithms , Machine Learning , Precision Medicine/methods , Support Vector Machine
2.
Metabolites ; 11(12)2021 Dec 06.
Article in English | MEDLINE | ID: mdl-34940605

ABSTRACT

Infection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and acute lung injury. Therefore, it is crucial to study breath-associated biofluids not only to investigate the breath's biochemical changes caused by SARS-CoV-2 infection, but also to discover potential biomarkers for the development of new diagnostic tools. In the present study, we performed an untargeted metabolomics approach using a bidimensional gas chromatography mass spectrometer (GCxGC-TOFMS) on exhaled breath condensate (EBC) from COVID-19 patients and negative healthy subjects to identify new potential biomarkers for the noninvasive diagnosis and monitoring of the COVID-19 disease. The EBC analysis was further performed in patients with acute or acute-on-chronic cardiopulmonary edema (CPE) to assess the reliability of the identified biomarkers. Our findings demonstrated that an abundance of EBC fatty acids can be used to discriminate COVID-19 patients and that they may have a protective effect, thus suggesting their potential use as a preventive strategy against the infection.

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