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dc.contributor.authorBrown, James A.L.
dc.contributor.authorNi Chonghaile, Triona
dc.contributor.authorMatchett, Kyle B.
dc.contributor.authorLynam-Lennon, Niamh
dc.contributor.authorKiely, Patrick A.
dc.date.accessioned2018-09-20T16:01:46Z
dc.date.available2018-09-20T16:01:46Z
dc.date.issued2016-10-20
dc.identifier.citationBrown, James A.L. Ni Chonghaile, Triona; Matchett, Kyle B.; Lynam-Lennon, Niamh; Kiely, Patrick A. (2016). Big data–led cancer research, application, and insights. Cancer Research 76 (21), 6167-6170
dc.identifier.issn0008-5472,1538-7445
dc.identifier.urihttp://hdl.handle.net/10379/10556
dc.description.abstractInsights distilled from integratingmultiple big-data or "omic" datasets have revealed functional hierarchies of molecular networks driving tumorigenesis and modifiers of treatment response. Identifying these novel key regulatory and dysregulated elements is now informing personalized medicine. Crucially, although there are many advantages to this approach, there are several key considerations to address. Here, we examine how this big data-led approach is impacting many diverse areas of cancer research, through review of the key presentations given at the Irish Association for Cancer Research Meeting and importantly how the results may be applied to positively affect patient outcomes.
dc.publisherAmerican Association for Cancer Research (AACR)
dc.relation.ispartofCancer Research
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectnext-generation
dc.subjectcells
dc.subjectmicrobiome
dc.subjectinterface
dc.subjectdiscovery
dc.subjecttumors
dc.subjectomics
dc.titleBig data–led cancer research, application, and insights
dc.typeArticle
dc.identifier.doi10.1158/0008-5472.can-16-0860
dc.local.publishedsourcehttp://cancerres.aacrjournals.org/content/76/21/6167.full.pdf
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