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diff grant.html @ 66:f14c34563ff8
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author | bshanks@bshanks.dyndns.org |
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date | Mon Apr 20 13:08:18 2009 -0700 (16 years ago) |
parents | f1f92feb3230 |
children | 20e4b29ddc99 |
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1.1 --- a/grant.html Sun Apr 19 16:24:10 2009 -0700
1.2 +++ b/grant.html Mon Apr 20 13:08:18 2009 -0700
1.3 @@ -468,6 +468,12 @@
1.4 Gene mtif2 is shown in the upper-right. Mtif2 captures MO’s upper-left boundary, but not its lower-right boundary. Mtif2
1.5 does not express very much on the medial surface. By adding together the values at each pixel in these two figures, we get
1.6 the lower-left image. This combination captures area MO much better than any single gene.
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1.8 +
1.9 +
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1.12 + Figure 6: todo liso
1.13 surface pixels based on their gene expression profiles. We achieved classification accuracy of about 81%19. As noted above,
1.14 however, a classifier that looks at all the genes at once isn’t as practically useful as a classifier that uses only a few genes.
1.15 Data-driven redrawing of the cortical map
1.16 @@ -539,6 +545,8 @@
1.17 # confirm with EMAGE, GeneAtlas, GENSAT, etc, to fight overfitting
1.18 # compare using clustering scores
1.19 # multivariate gradient similarity
1.20 +# deep belief nets
1.21 +# note: slice artifact
1.22 Bibliography & References Cited
1.23 [1]Tanya Barrett, Dennis B. Troup, Stephen E. Wilhite, Pierre Ledoux, Dmitry Rudnev, Carlos Evangelista, Irene F.
1.24 Kim, Alexandra Soboleva, Maxim Tomashevsky, and Ron Edgar. NCBI GEO: mining tens of millions of expression