

Maximal Information-based Nonparametric Exploration (MINE) statistics - kudwitt
http://www.exploredata.net/

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baltcode
So the idea is pretty simple. Basically, they are asking, what is the mutual
information between two variables? For those without a background in Inf.
Theory: mutual information basically measures the amount of detail involved in
knowing the two variables separately vs. knowing the combination of them. So
if one variable can be predicted from the other perfectly, the mutual
information is high, since knowing the two variables separately you would have
to keep a lot of details, while knowing them together, you only need to know
one. Information can be measured based on the discretization applied to the
variables. They basically say if you look at all discretizations, then you can
see if there is any way the two variables are related. Of course, they have to
resort to an approximate algorithm. The idea is simple, and really, it is not
new. I think a lot of non-parametric technicques try do the same. There
software can be downloaded and I'd like to see its complexity and performance.
I didn't find too much inf. on big O complexity or run times.

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tocomment
Can anyone explain what the big deal is on this? Would it benefit my biomed
research?

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epistasis
They claim a non-parametric correlation metric that can capture all sorts of
dependencies that current metrics may miss. This is kind of cool, and may
eventually prove to be a big deal. If you're trying to find out what your new,
unstudied, gene of interest does, it may help you find genes that it is co-
expressed with, and give a hint at function.

In reality it's a big deal right now because they're marketing their paper as
if it were a movie, with press releases, a professionally produced trailer,
and well-designed web sites. Personally, it comes across as trying way too
hard, but whenever Broad rediscovers some well-hashed field they like to play
it up as if they were Newton releasing calculus upon the world.

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chargrilled
It's a shame the original paper seems to be stuck behind a journal paywall.

Does anyone have access to a preprint?

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chapmanb
The PDF is currently available, although I couldn't find a direct link so it
may be a temporary mistake:

<http://www.sciencemag.org/content/334/6062/1518.full.pdf>

Supporting online material:

[http://www.sciencemag.org/content/suppl/2011/12/14/334.6062....](http://www.sciencemag.org/content/suppl/2011/12/14/334.6062.1518.DC1/Reshef.SOM.pdf)

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chargrilled
I get a "Subscribe/Join AAAS or Buy Access to This Article to View Full Text."
for the PDF page.

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kudwitt
Here is the paper: <http://www.mediafire.com/?4xc57v4jfmdc1k3>

