Content-based search on microarray gene expression databases

The rapid growth of gene expression databases has created a need for content-based searches as an alternative to unstructured database queries using keyword- or metadata-based searches. Content-based searching is the ability to retrieve all experiments with similar gene expression patterns in a database regardless of the biological annotations provided for these experiments. While this concept is still in its infancy in a general context, in this thesis we focus on applying it to a specific subset of gene expression datasets, by only querying experiments involving time-series expression profiles. To this end, we propose a novel experiment fingerprinting scheme obtained by clustering expression profiles, for content-based searching of time-series microarray experiments. To determine the retrieval ability of the proposed scheme, we performed a simulated information retrieval task on a large set of microarray experiments gathered from a public repository. The relevance between any two experiments was then defined using their commonalities based on annotated disease associations. The results showed that relevant experiments can be more successfully retrieved using this new method compared with traditional differential expression-based methods.