1.1 --- a/grant.txt Tue Apr 14 02:23:38 2009 -0700 1.2 +++ b/grant.txt Tue Apr 14 02:31:37 2009 -0700 1.3 @@ -229,13 +229,19 @@ 1.4 1.5 The simplest way to use information theory is on discrete data, so we discretized our gene expression data by creating, for each gene, five thresholded binary masks of the gene data. For each gene, we created a binary mask of its expression levels over pixels using each of these thresholds: the mean of that gene, the mean minus one standard deviation, the mean minus two standard deviations, the mean plus one standard deviation, the mean plus two standard deviations. 1.6 1.7 -Now, for each region, we ran a forward stepwise procedure which attempted to find pairs of gene expression binary masks such that the conditional entropy of the target area's binary mask, conditioned upon the pair of gene expression binary masks, is minimized. 1.8 - 1.9 -This finds pairs of genes which are most informative, at least at these discretization thresholds. 1.10 +Now, for each region, we created and ran a forward stepwise procedure which attempted to find pairs of gene expression binary masks such that the conditional entropy of the target area's binary mask, conditioned upon the pair of gene expression binary masks, is minimized. 1.11 + 1.12 +This finds pairs of genes which are most informative (at least at these discretization thresholds) relative to the question, "Is this surface pixel a member of the target area?". 1.13 1.14 todo: fig 1.15 1.16 \vspace{0.3cm}**Gradient similarity** 1.17 +We noticed that the previous two scoring methods, which are pointwise, often found genes whose pattern of expression did not look similar in shape to the target region. Fort his reason we designed a non-pointwise local scoring method to detect when a gene had a pattern of expression which looked like it had a boundary whose shape is similar to the shape of the target region. 1.18 + 1.19 + 1.20 + 1.21 +had shape of the pattern of expression did not seem to match the shape of the target area. 1.22 + 1.23 todo 1.24 1.25