For decades, cancer biomarker research has been plagued by tough biological, technical, methodological, statistical, and computational challenges. Recent technological advancements and computational modeling approaches hold promise to greatly accelerate the pace of biomarker discovery and development. Yet BIG biomarker data generates new challenges including the need for infrastructure, better statistical and analytical tools, and common data models to name a few. Presently, most cancer biomarker-related data collections are highly dispersed, which his hinders data integration often needed to maximize the full potential of these data sets. There is a general consensus in the research community that the demand for FAIR (Findable, Accessible, Interoperable, Reusable) and FIT (Fit-for-purpose) biomarker data has far outpaced acquisition.
On February 8-9th, 2018, the National Cancer Institute (NCI) conveened a think-tank meeting in Rockville, MD, to examine these persistent problems and discuss potential solutions.
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