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A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways

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dc.contributor.authorBardozzo, Francesco
dc.contributor.authorLio', Pietro
dc.contributor.authorTagliaferri, Roberto
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
dc.date.accessioned2020-01-28T00:32:27Z
dc.date.available2020-01-28T00:32:27Z
dc.date.issued2020
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.identifier.doi10.17863/CAM.48430
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301349
dc.language.isoeng
dc.publisher.urlhttp://dx.doi.org/10.1093/bioinformatics/btaa966
dc.rightsAll rights reserved
dc.subjectq-bio.MN
dc.subjectq-bio.MN
dc.subjectcs.LG
dc.subjectstat.ML
dc.titleA machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
dc.typeConference Object
prism.endingPage13
prism.publicationNameCoRR
prism.startingPage13
prism.volume2016
pubs.conference-finish-date2016-09-03
pubs.conference-name13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
pubs.conference-start-date2016-09-01
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeConference Paper/Proceeding/Abstract
rioxxterms.versionAM
rioxxterms.versionofrecord10.17863/CAM.48430

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