1.1 Binary file grant.pdf has changed

2.1 --- a/grant.txt Mon Apr 20 17:32:13 2009 -0700 2.2 +++ b/grant.txt Mon Apr 20 17:33:37 2009 -0700 2.3 @@ -99,7 +99,7 @@ 2.4 \item Clusters: will be described later 2.5 \end{itemize} 2.6 2.7 -Gene Finder is different from our Aim 1 in at least three ways. First, Gene Finder finds only single genes, whereas we will also look for combinations of genes. Second, gene finder can only use overexpression as a marker, whereas we will also search for underexpression. Third, Gene Finder uses a simple pointwise score\footnote{"Expression energy ratio", which captures overexpression.}, whereas we will also use geometric scores such as gradient similarity. Figures \ref{MOcombo}, \ref{hole}, and \ref{AUDgeometry} in Preliminary Studies section contains evidence that each of our three choices is the right one. 2.8 +Gene Finder is different from our Aim 1 in at least three ways. First, Gene Finder finds only single genes, whereas we will also look for combinations of genes. Second, gene finder can only use overexpression as a marker, whereas we will also search for underexpression. Third, Gene Finder uses a simple pointwise score\footnote{"Expression energy ratio", which captures overexpression.}, whereas we will also use geometric scores such as gradient similarity. Figures \ref{MOcombo}, \ref{hole}, and \ref{AUDgeometry} in the Preliminary Studies section contains evidence that each of our three choices is the right one. 2.9 2.10 \cite{chin_genome-scale_2007} looks at the mean expression level of genes within anatomical regions, and applies a Student's t-test with Bonferroni correction to determine whether the mean expression level of a gene is significantly higher in the target region. Like AGEA, this is a pointwise measure (only the mean expression level per pixel is being analyzed), it is not being used to look for underexpression, and does not look for combinations of genes. 2.11