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dc.contributor.authorDe Meulder, Bertrand
dc.contributor.authorLefaudeux, Diane
dc.contributor.authorBansal, Aruna T
dc.contributor.authorMazein, Alexander
dc.contributor.authorChaiboonchoe, Amphun
dc.contributor.authorAhmed, Hassan
dc.contributor.authorBalaur, Irina
dc.contributor.authorSaqi, Mansoor
dc.contributor.authorPellet, Johann
dc.contributor.authorBallereau, Stéphane
dc.contributor.authorLemonnier, Nathanaël
dc.contributor.authorSun, Kai
dc.contributor.authorPandis, Ioannis
dc.contributor.authorYang, Xian
dc.contributor.authorBatuwitage, Manohara
dc.contributor.authorKretsos, Kosmas
dc.contributor.authorvan Eyll, Jonathan
dc.contributor.authorBedding, Alun
dc.contributor.authorDavison, Timothy
dc.contributor.authorDodson, Paul
dc.contributor.authorLarminie, Christopher
dc.contributor.authorPostle, Anthony
dc.contributor.authorCorfield, Julie
dc.contributor.authorDjukanovic, Ratko
dc.contributor.authorChung, Kian F
dc.contributor.authorAdcock, Ian M
dc.contributor.authorGuo, Yi-Ke
dc.contributor.authorSterk, Peter J
dc.contributor.authorManta, Alexander
dc.contributor.authorRowe, Anthony
dc.contributor.authorBaribaud, Frédéric
dc.contributor.authorAuffray, Charles
dc.date.accessioned2018-05-30T06:20:32Z
dc.date.available2018-05-30T06:20:32Z
dc.date.issued2018-05-29
dc.identifier.citationBMC Systems Biology. 2018 May 29;12(1):60
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/276293
dc.description.abstractAbstract Background Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-‘omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-‘omics signatures of disease states. Methods The framework is divided into four major steps: dataset subsetting, feature filtering, ‘omics-based clustering and biomarker identification. Results We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-‘omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions This framework will help health researchers plan and perform multi-‘omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
dc.titleA computational framework for complex disease stratification from multiple large-scale datasets
dc.typeJournal Article
dc.date.updated2018-05-30T06:20:26Z
dc.language.rfc3066en
dc.rights.holderThe Author(s).
dc.identifier.doi10.17863/CAM.23582
rioxxterms.versionofrecord10.1186/s12918-018-0556-z


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