International Journal of Computer Vision
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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Research in the field of content-based retrieval has primarily focused on image, video an audio information. In this paper, we demonstrate content-based retrieval in a new data domain called gene expression data derive 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, an analyze similarity between gene profiles by the relative amounts of twists an turns produce in a higher-imensional curve forme from the projection of the individual gene profiles. Scale-space analysis is use to detect the sharp twists an turns an 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 escriptions 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.