Little-Known Algorithm May Help Resolve Dimensionality Challenge of Array Analysis | GenomeWeb
An oft-cited challenge in analyzing microarray experiments is the so-called “extreme dimensionality” of the data that arises when tens of thousands of variables are interrogated across relatively few samples.
 
Researchers have developed an arsenal of clustering and classification methods to address this issue, and countless papers have been published on the subject with the goal of improving methods to correlate groups of samples with particular phenotypes of interest.
 

Get the full story with
GenomeWeb Premium

Only $95 for the
first 90 days*

A trial upgrade to GenomeWeb Premium gives you full site access, interest-based email alerts, access to archives, and more. Never miss another important industry story.

Try GenomeWeb Premium now.

Already a GenomeWeb Premium member? Login Now.
Or, See if your institution qualifies for premium access.

*Before your trial expires, we’ll put together a custom quote with your long-term premium options.

Not ready for premium?

Register for Free Content
You can still register for access to our free content.