1.1 --- a/.hgignore Sat Apr 11 19:12:32 2009 -0700 1.2 +++ b/.hgignore Sat Apr 11 19:35:08 2009 -0700 1.3 @@ -14,6 +14,7 @@ 1.4 *.aux 1.5 *.xref 1.6 *.lg 1.7 +*.4?? 1.8 1.9 syntax: regexp 1.10 .*\#.*\#$

2.1 --- a/grant.html Sat Apr 11 19:12:32 2009 -0700 2.2 +++ b/grant.html Sat Apr 11 19:35:08 2009 -0700 2.3 @@ -108,12 +108,12 @@ 2.4 to label more than a few genes. Therefore, we must select only a few genes as 2.5 features. 2.6 Principle 3: Use geometry in feature selection 2.7 - When doing feature selection with score-based methods, the simplest thing to 2.8 - do would be to score the performance of each voxel by itself and then combine 2.9 - these scores; this is pointwise scoring. A more powerful approach is to also use 2.10 - information about the geometric relations between each voxel and its neighbors; 2.11 - this requires non-pointwise, local scoring methods. See Preliminary Results for 2.12 - evidence of the complementary nature of pointwise and local scoring methods. 2.13 + When doing feature selection with score-based methods, the simplest thing to do 2.14 + would be to score the performance of each voxel by itself and then combine these 2.15 + scores (pointwise scoring). A more powerful approach is to also use information 2.16 + about the geometric relations between each voxel and its neighbors; this requires 2.17 + non-pointwise, local scoring methods. See Preliminary Results for evidence of 2.18 + the complementary nature of pointwise and local scoring methods. 2.19 Principle 4: Work in 2-D whenever possible 2.20 There are many anatomical structures which are commonly characterized in 2.21 terms of a two-dimensional manifold. When it is known that the structure that 2.22 @@ -121,6 +121,8 @@ 2.23 the analysis algorithm to take advantage of this prior knowledge. In addition, 2.24 it is easier for humans to visualize and work with 2-D data. 2.25 Therefore, when possible, the instances should represent pixels, not voxels. 2.26 + Aim 2 2.27 + todo 2.28 3 2.29 2.30 Aim 3 2.31 @@ -173,6 +175,7 @@ 2.32 conceivable that the methods we propose to develop could be used to suggest 2.33 modifications to the human cortical map as well. 2.34 Related work 2.35 + todo 2.36 Preliminary work 2.37 Justification of principles 1 thur 3 2.38 Principle 1: Combinatorial gene expression 2.39 @@ -198,8 +201,6 @@ 2.40 useful. 2.41 The requirement to find combinations of only a small number of genes limits 2.42 us from straightforwardly applying many of the most simple techniques from 2.43 - the field of supervised machine learning. In the parlance of machine learning, 2.44 - our task combines feature selection with supervised learning. 2.45 __________________________ 2.46 1“WW, C2 and coiled-coil domain containing 1”; EntrezGene ID 211652 2.47 2“mitochondrial translational initiation factor 2”; EntrezGene ID 76784 2.48 @@ -228,6 +229,8 @@ 2.49 genes which (individually) best match area AUD, according to gradient similar- 2.50 ity. From left to right and top to bottom, the genes are Ssr1, Efcbp1, Aph1a, 2.51 Ptk7, Aph1a again, and Lepr 2.52 + the field of supervised machine learning. In the parlance of machine learning, 2.53 + our task combines feature selection with supervised learning. 2.54 Principle 3: Use geometry 2.55 To show that local geometry can provide useful information that cannot be 2.56 detected via pointwise analyses, consider Fig. . The top row of Fig. displays

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