Abstract:
: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.
journal_name
Comput Struct Biotechnol Jjournal_title
Computational and structural biotechnology journalauthors
Antonakoudis A,Barbosa R,Kotidis P,Kontoravdi Cdoi
10.1016/j.csbj.2020.10.011subject
Has Abstractpub_date
2020-10-16 00:00:00pages
3287-3300issn
2001-0370pii
S2001-0370(20)30433-5journal_volume
18pub_type
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