Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Elettra Barberis, Shahzaib Khoso, Antonio Sica, Marco Falasca, Alessandra Gennari, Francesco Dondero, Antreas Afantitis, Marcello Manfredi

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish
Article number11269
JournalInternational Journal of Molecular Sciences
Volume23
Issue number19
DOIs
Publication statusPublished - Oct 2022

Keywords

  • artificial intelligence
  • biomarkers
  • machine learning
  • metabolomics
  • precision medicine

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