A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
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Publication Date
2020Journal Title
CIBB
Conference Name
13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
Volume
2016
Pages
13-13
Type
Conference Object
This Version
AM
Metadata
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Bardozzo, F., Lio, P., & Tagliaferri, R. (2020). A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways. CIBB, 2016 13-13. https://doi.org/10.17863/CAM.48430
Abstract
In this work, a machine learning approach for identifying the multi-omics
metabolic regulatory control circuits inside the pathways is described.
Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . This is a consequence of the alternation of the omic values of codon usage and protein abundance along with the circuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features are shown as an example.
Identifiers
External DOI: https://doi.org/10.17863/CAM.48430
This record's URL: https://www.repository.cam.ac.uk/handle/1810/301349
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