The genetic analysis of spatial patterns of gene expression relies on the direct visualization of the presence or absence of gene products at a given developmental stage (time) of a developing animal. In order to facility effect use of the fast growing collection of fruit fly (Drosophila) embryonic gene patterns from high throughput experiments, a computational framework for finding genes with overlapping expression patterns is being developed. The first step is to develop a learning system to identify automatically the developmental stage by image analysis. Once the developmental stage is determined, expression patterns are compared for spatial overlaps. The works deals with both the representation and manipulation of advanced data types and are intended to build a novel framework using machine learning techniques. In addition to facilitating both machine learning and molecular biology research, the work will engage students and the database used as a teaching resource.
