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dc.contributor.authorBardozzo, Francesco
dc.contributor.authorLio', Pietro
dc.contributor.authorTagliaferri, Roberto
dc.date.accessioned2020-01-28T00:32:27Z
dc.date.available2020-01-28T00:32:27Z
dc.date.issued2020-01-13
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 pathways
dc.typeConference Object
prism.endingPage13
prism.publicationNameCIBB
prism.startingPage13
prism.volume2016
dc.identifier.doi10.17863/CAM.48430
rioxxterms.versionofrecord10.17863/CAM.48430
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.conference-name13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
pubs.conference-start-date2016-09-01
cam.orpheus.successTue Feb 01 18:59:13 GMT 2022 - Embargo updated
cam.orpheus.counter60
pubs.conference-finish-date2016-09-03
rioxxterms.freetoread.startdate2021-12-31


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