A2 Katsausartikkeli tieteellisessä aikauslehdessä

Deep learning meets metabolomics: a methodological perspective

Julkaisun tekijät: Partho Sen, Santosh Lamichhane, Vivek B Mathema, Aidan McGlinchey, Alex M Dickens, Sakda Khoomrung, Matej Orešič

Kustantaja: Oxford Academic

Paikka: Turku

Julkaisuvuosi: 2020

Journal: Briefings in Bioinformatics

eISSN: 1477-4054

DOI: http://dx.doi.org/10.1093/bib/bbaa204


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.

Last updated on 2021-24-06 at 12:08