Analysis Priorities

Actively support crowd-sourcing challenges

To stimulate learning as much as possible, as quickly as possible, the data cloud could have a utility where interested parties could pose "crowd-sourcing" challenges, e,g, Kaggle. Indeed, Harold Varmus, NCI & leaders in cancer & genomics could pose the leading questions they would like bright people to take a run at answering, e.g. Hilbert's 23 problems

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Analysis Priorities

Integrative Analysis of molecular datatypes for a given sample

A sample could be analyzed for DNA sequence variations, structural variations, CNVs, Gene or transcript isoform expression, genome-wide methylation patterns, ChIP-seq for specific transcription factors, metabolomic or proteomic analysis, and other molecular profiles. A framework that allows a researcher to readily identify all molecular data types associated with a particular sample and integrate the results of such analyses ...more »

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Data Priorities

Connect data with available specimens for follow-up studies

Mining cancer data in the cloud is great, but to enable ongoing research there should be a connection to specimens so researchers can pursue followup studies. This will require storing data about specimens from studies such as TCGA - where they are, how they can be accessed and what consent they are governed by. Just as the data from publications should be made available to allow reproduction of results, so should samples ...more »

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