A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä

Deep learning meets metabolomics: a methodological perspective




TekijätPartho Sen, Santosh Lamichhane, Vivek B Mathema, Aidan McGlinchey, Alex M Dickens, Sakda Khoomrung, Matej Orešič

KustantajaOxford Academic

KustannuspaikkaTurku

Julkaisuvuosi2021

JournalBriefings in Bioinformatics

Artikkelin numerobbaa204

eISSN1477-4054

DOIhttps://doi.org/10.1093/bib/bbaa204


Tiivistelmä

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 2024-26-11 at 15:43