Machine learning applications in genome-scale metabolic modeling

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Genome-scale metabolic modeling and simulation have been widely employed in biological studies and biotechnological applications due to their powerful capabilities of estimating metabolic fluxes at the systems level. In recent years, machine learning (ML) has been beginning to be applied to the reconstruction and analysis of genome-scale metabolic models (GEMs) to improve their quality. Also, ML has been used to diversify the utilization of information derived from genome-scale metabolic modeling and simulation. Recent studies have shown that machine learning can improve predictive performance and data coverage of GEMs. Also, genome-scale metabolic modeling and simulation provide interpretability of ML applications. Although many biological data still need to be made suitable for ML applications, it is expected that ML will be increasingly applied to GEMs to further improve the practical use and find new applications of GEMs.
Publisher
Elsevier BV
Issue Date
2021-03
Language
English
Citation

Current Opinion in Systems Biology, v.25, pp.42 - 49

ISSN
2452-3100
DOI
10.1016/j.coisb.2021.03.001
URI
http://hdl.handle.net/10203/282331
Appears in Collection
CBE-Journal Papers(저널논문)
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