Content-Based Search on Time-Series Microarray Databases

We study, for the first time, the problem of content-based searching of time-series microarray experiments in large-scale gene expression databases. The problem is approached as an information retrieval task where an entire ex-periment is taken as the query and searched through a collection of previous experiments. The relevant experiments are required to be retrieved based on the content similarity rather than their meta-data descriptions. A comparison of different fingerprinting and distance computation schemes is presented over a retrieval framework based on the differential expression of genes in varying time points.