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Deep learning meets...
Deep learning meets metabolomics : a methodological perspective
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- 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
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- Lamichhane, Santosh (author)
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
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- 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
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- McGlinchey, Aidan J., 1984- (author)
- Örebro universitet,Institutionen för medicinska vetenskaper
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- Dickens, Alex M. (author)
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
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- 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
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- Oresic, Matej, 1967- (author)
- Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
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(creator_code:org_t)
- 2020-09-17
- 2021
- English.
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In: Briefings in Bioinformatics. - : Oxford University Press. - 1467-5463 .- 1477-4054. ; 22:2, s. 1531-1542
- Related links:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Keyword
- Artificial intelligence
- deep learning
- genome-scale metabolic modelling
- lipidomics
- machine learning
- metabolism
- metabolomics
Publication and Content Type
- ref (subject category)
- for (subject category)
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