1.1 --- a/grant.txt Mon Apr 13 03:07:26 2009 -0700 1.2 +++ b/grant.txt Mon Apr 13 03:10:37 2009 -0700 1.3 @@ -138,7 +138,7 @@ 1.4 === Related work === 1.5 There does not appear to be much work on the automated analysis of spatial gene expression data. 1.6 1.7 -There is a substantial body of work on the analysis of gene expression data, however, most of this concerns gene expression data which is not fundamentally spatial, for example, microarray datasets. In some cases, a few locations have been sampled, but such a dataset is still of a fundamentally different character than a dataset containing a large grid of sampling points distributed over space. In relating gene expression to anatomy, it is the spatial aspects of the problem which are the most important. 1.8 +There is a substantial body of work on the analysis of gene expression data, however, most of this concerns gene expression data which is not fundamentally spatial, for example, microarray datasets. 1.9 1.10 As noted above, there has been much work on both supervised learning and clustering, and there are many available algorithms for each. Many of these algorithms are flexible enough to accomodate new scoring measures; and the performance of most of the algorithms is greatly affected by preprocessing and by the choice of which representation to use for feature values. We think it likely that for this application, the development of domain-specific scoring measures (such as gradient similarity, which is discussed in Preliminary Work) will be necessary in order to achieve the best results. In essence, the machine learning community has provided algorithms, but the scientist must provide a framework for representing the problem domain, and the way that this framework is set up has a large impact on performance. Creating a good framework can require creatively reconceptualizing the problem domain, and is not merely a mechanical "fine-tuning" of numerical parameters. Therefore, the completion of Aims 1 and 2 involves more than just reimplementing an existing algorithm, and more than just choosing between a set of existing algorithms, and will constitute a substantial contribution to biology. 1.11