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dc.contributor.authorBardozzo, Francescoen
dc.contributor.authorLio, Pietroen
dc.contributor.authorTagliaferri, Robertoen
dc.date.accessioned2020-01-28T00:32:27Z
dc.date.available2020-01-28T00:32:27Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301349
dc.description.abstractIn 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.
dc.rightsAll rights reserved
dc.titleA machine learning approach to investigate regulatory control circuits in bacterial metabolic pathwaysen
dc.typeConference Object
prism.endingPage13
prism.startingPage13
prism.volume2016en
dc.identifier.doi10.17863/CAM.48430
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
rioxxterms.typeConference Paper/Proceeding/Abstracten
pubs.conference-name13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatisticsen
pubs.conference-start-date2016-09-01en
cam.orpheus.counter60*
rioxxterms.freetoread.startdate2023-01-27


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