Bayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Disease

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dc.citation.firstpage917en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleJournal of Alzheimer's Diseaseen_US
dc.citation.lastpage925en_US
dc.citation.volumeNumber44en_US
dc.contributor.authorRembach, Alanen_US
dc.contributor.authorStingo, Francesco C.en_US
dc.contributor.authorPeterson, Christineen_US
dc.contributor.authorVannucci, Marinaen_US
dc.contributor.authorDo, Kim-Anhen_US
dc.contributor.authorWilson, William J.en_US
dc.contributor.authorMacaulay, S. Lanceen_US
dc.contributor.authorRyan, Timothy M.en_US
dc.contributor.authorMartins, Ralph N.en_US
dc.contributor.authorAmes, Daviden_US
dc.contributor.authorMasters, Colin L.en_US
dc.contributor.authorDoecke, James D.en_US
dc.contributor.authorThe AIBL Research Groupen_US
dc.date.accessioned2017-06-06T19:07:23Zen_US
dc.date.available2017-06-06T19:07:23Zen_US
dc.date.issued2015en_US
dc.description.abstractWith different approaches to finding prognostic or diagnostic biomarkers for Alzheimer's disease (AD), many studies pursue only brief lists of biomarkers or disease specific pathways, potentially dismissing information from groups of correlated biomarkers. Using a novel Bayesian graphical network method, with data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the aim of this study was to assess the biological connectivity between AD associated blood-based proteins. Briefly, three groups of protein markers (18, 37, and 48 proteins, respectively) were assessed for the posterior probability of biological connection both within and between clinical classifications. Clinical classification was defined in four groups: high performance healthy controls (hpHC), healthy controls (HC), participants with mild cognitive impairment (MCI), and participants with AD. Using the smaller group of proteins, posterior probabilities of network similarity between clinical classifications were very high, indicating no difference in biological connections between groups. Increasing the number of proteins increased the capacity to separate both hpHC and HC apart from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities shifting from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 48 protein group. Using this approach, we identified beta-2 microglobulin (β2M) as a potential master regulator of multiple proteins across all classifications, demonstrating that this approach can be used across many data sets to identify novel insights into diseases like AD.en_US
dc.identifier.citationRembach, Alan, Stingo, Francesco C., Peterson, Christine, et al.. "Bayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Disease." <i>Journal of Alzheimer's Disease,</i> 44, no. 3 (2015) IOS Press: 917-925. https://doi.org/10.3233/JAD-141497.en_US
dc.identifier.doihttps://doi.org/10.3233/JAD-141497en_US
dc.identifier.urihttps://hdl.handle.net/1911/94819en_US
dc.language.isoengen_US
dc.publisherIOS Pressen_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IOS.en_US
dc.subject.keywordAlzheimer's diseaseen_US
dc.subject.keywordBayesianen_US
dc.subject.keywordbiomarkersen_US
dc.subject.keywordgraphical networksen_US
dc.subject.keywordimputationen_US
dc.titleBayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Diseaseen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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