Making sense out of massive data by going beyond differential expression

With the rapid growth of publicly available high-throughput transcriptomic data, there is increasing recognition that large sets of such data can be mined to better understand disease states and mechanisms. Prior gene expression analyses, both large and small, have been dichotomous in nature, in whi...

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Bibliographic Details
Main Authors: Schmid, Patrick Raphael (Contributor), Palmer, Nathan Patrick (Contributor), Kohane, Isaac (Contributor), Berger, Bonnie (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: National Academy of Sciences, 2012-10-16T14:11:56Z.
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