Automatically identifying and annotating mouse embryo gene expression patterns

Motivation: Deciphering the regulatory and developmental mechanisms for multicellular organisms requires detailed knowledge of gene interactions and gene expressions. The availability of large datasets with both spatial and ontological annotation of the spatio-temporal patterns of gene expression in mouse embryo provides a powerful resource to discover the biological function of embryo organization. Ontological annotation of gene expressions consists of labelling images with terms from the anatomy ontology for mouse development. If the spatial genes of an anatomical component are expressed in an image, the image is then tagged with a term of that anatomical component. The current annotation is done manually by domain experts, which is both time consuming and costly. In addition, the level of detail is variable, and inevitably errors arise from the tedious nature of the task. In this article, w