Probabilistic retrieval and visualization of biologically relevant microarray experiments

http://bioinformatics.oxfordjournals.org/content/25/12/i145.full

content based search instead of searching through annotations


so give gene expression data set instead of just typing words

Motivation: As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.

Results: We use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques.

Availability: The code is available from http://www.cis.hut.fi/projects/mi/software/ismb09.

Contact: jose.caldas@tkk.fi


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http://www.biomedcentral.com/1471-2164/10/411

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery

Conclusion

GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at http://cgs.pharm.kyoto-u.ac.jp/services/network webcite.

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CellMontage: similar expression profile search server

Summary: The establishment and rapid expansion of microarray databases has created a need for new search tools. Here we present CellMontage, the first server for expression profile similarity search over a large database—69 000 microarray experiments derived from NCBI's; GEO site. CellMontage provides a novel, content-based search engine for accessing gene expression data. Microarray experiments with similar overall expression to a user-provided expression profile (e.g. microarray experiment) are computed and displayed—usually within 20 s. The core search engine software is downloadable from the site.

http://cellmontage.cbrc.jp


http://bioinformatics.oxfordjournals.org/content/23/22/3103.abstract