Content-based retrieval in gene expression databases

Research in the field of content-based retrieval has primarily focused on image, video and audio information. In this paper, we demonstrate content-based retrieval in a new data domain called gene expression data derived from gene chip images. In particular, we consider the problem of retrieving functionally similar genes from a database based on the pattern of variation of the expression of genes over time. Specifically, we model the time-varying gene expression patterns as curves, and analyze similarity between gene profiles by the relative amounts of twists and turns produced in a higher-dimensional curve formed from the projection of the individual gene profiles. Scale-space analysis is used to detect the sharp twists and turns and their relative strength with respect to the component curves is estimated to form a shape similarity measure between gene profiles. The higher-dimensional curves also form prototypical descriptions of the individual gene profiles, serving as a way to index the database using clustering. Functionally similar genes are then identified using scale-space distance metric on the cluster prototypes.