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dc.contributor.authorNazri, Azree
dc.contributor.authorLio, Pietro
dc.date.accessioned2018-09-20T12:03:34Z
dc.date.available2018-09-20T12:03:34Z
dc.date.issued2012
dc.identifier.issn1932-6203
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/280446
dc.description.abstractThe output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCells
dc.subjectAnimals
dc.subjectMammals
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectColorectal Neoplasms
dc.subjectArea Under Curve
dc.subjectBayes Theorem
dc.subjectReproducibility of Results
dc.subjectComputational Biology
dc.subjectComputer Simulation
dc.subjectSoftware
dc.subjectDatabases, Genetic
dc.subjectFemale
dc.subjectGene Regulatory Networks
dc.subjectMeta-Analysis as Topic
dc.titleInvestigating meta-approaches for reconstructing gene networks in a mammalian cellular context.
dc.typeArticle
prism.issueIdentifier1
prism.publicationDate2012
prism.publicationNamePLoS One
prism.startingPagee28713
prism.volume7
dc.identifier.doi10.17863/CAM.27817
dcterms.dateAccepted2011-11-14
rioxxterms.versionofrecord10.1371/journal.pone.0028713
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2012-01-09
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
dc.identifier.eissn1932-6203
rioxxterms.typeJournal Article/Review
cam.issuedOnline2012-01-09


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International