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dc.contributor.authorParmar, V
dc.contributor.authorLió, P
dc.date.accessioned2019-05-27T23:30:15Z
dc.date.available2019-05-27T23:30:15Z
dc.date.issued2019
dc.identifier.issn1860-949X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/293143
dc.description.abstractThe analysis of biological networks is characterized by the definition of precise linear constraints used to cumulatively reduce the solution space of the computed states of a multi-omic (for instance metabolic, transcriptomic and proteomic) model. In this paper, we attempt, for the first time, to combine metabolic modelling and networked Cox regression, using the metabolic model of the bacterium Helicobacter Pylori. This enables a platform both for quantitative analysis of networked regression, but also testing the findings from network regression (a list of significant vectors and their networked relationships) on in vivo transcriptomic data. Data generated from the model, using flux balance analysis to construct a Pareto front, specifically, a trade-off of Oxygen exchange and growth rate and a trade-off of Carbon Dioxide exchange and growth rate, is analysed and then the model is used to quantify the success of the analysis. It was found that using the analysis, reconstruction of the initial data was considerably more successful than a pure noise alternative. Our methodological approach is quite general and it could be of interest for the wider community of complex networks researchers; it is implemented in a software tool, MoNeRe, which is freely available through the Github platform.
dc.publisherSpringer International Publishing
dc.rightsAll rights reserved
dc.titleMulti-omic network regression: Methodology, tool and case study
dc.typeArticle
prism.endingPage624
prism.publicationDate2019
prism.publicationNameStudies in Computational Intelligence
prism.startingPage611
prism.volume813
dc.identifier.doi10.17863/CAM.40292
rioxxterms.versionofrecord10.1007/978-3-030-05414-4_49
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-01-01
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
dc.identifier.eissn1860-9503
dc.publisher.urlhttps://doi.org/10.1007/978-3-030-05414-4
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-12-05
rioxxterms.freetoread.startdate2020-01-01


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