Deep learning meets metabolomics: a methodological perspective

Deep learning meets metabolomics : a methodological perspective

Sen, Partho, 1983- (author): Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

Lamichhane, Santosh (author): Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

Mathema, Vivek B. (author): Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

McGlinchey, Aidan J., 1984- (author): Örebro universitet,Institutionen för medicinska vetenskaper

Dickens, Alex M. (author): Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

Khoomrung, Sakda (author): Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

Oresic, Matej, 1967- (author): Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

(creator_code:org_t)

2020-09-17
2021
English.
In: Briefings in Bioinformatics. - : Oxford University Press. - 1467-5463 .- 1477-4054. ; 22:2, s. 1531-1542

Related links:: https://doi.org/10.1...; show more...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

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.

By the author/editor: Sen, Partho, 198 ...; Lamichhane, Sant ...; Mathema, Vivek B ...; McGlinchey, Aida ...; Dickens, Alex M.; Khoomrung, Sakda; show more...; Oresic, Matej, 1 ...; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Biological Scien ...; and Bioinformatics a ...

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