Deep learning meets metabolomics: a methodological perspective.

Autor: Sen P; Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden., Lamichhane S; Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland., Mathema VB; Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand., McGlinchey A; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden., Dickens AM; Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland., Khoomrung S; Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.; Center for Innovation in Chemistry (PERCH), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand., Orešič M; Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2021 Mar 22; Vol. 22 (2), pp. 1531-1542.
DOI: 10.1093/bib/bbaa204
Abstrakt: Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.
(© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje