The era of big data: Genome-scale modelling meets machine learning.
| Autor: | Antonakoudis A; Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom., Barbosa R; Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom., Kotidis P; Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom., Kontoravdi C; Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom. |
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| Jazyk: | angličtina |
| Zdroj: | Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2020 Oct 16; Vol. 18, pp. 3287-3300. Date of Electronic Publication: 2020 Oct 16 (Print Publication: 2020). |
| DOI: | 10.1016/j.csbj.2020.10.011 |
| Abstrakt: | With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2020 The Author(s).) |
| Databáze: | MEDLINE |
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