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- W2093020263 abstract "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." @default.
- W2093020263 created "2016-06-24" @default.
- W2093020263 creator A5057284370 @default.
- W2093020263 date "2004-10-10" @default.
- W2093020263 modified "2023-10-14" @default.
- W2093020263 title "Content-based retrieval in gene expression databases" @default.
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- W2093020263 doi "https://doi.org/10.1145/1027527.1027710" @default.
- W2093020263 hasPublicationYear "2004" @default.
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