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Motivation: Public and private repositories of experimental data are growingto sizes that require dedicated methods for finding relevant data. To improveon the state of the art of keyword searches from annotations, methods forcontent-based retrieval have been proposed. In the context of gene expressionexperiments, most methods retrieve gene expression profiles, requiring eachexperiment to be expressed as a single profile, typically of case vs. control.A more general, recently suggested alternative is to retrieve experiments whosemodels are good for modelling the query dataset. However, for very noisy andhigh-dimensional query data, this retrieval criterion turns out to be verynoisy as well.  Results: We propose doing retrieval using a denoised model of the querydataset, instead of the original noisy dataset itself. To this end, weintroduce a general probabilistic framework, where each experiment is modelledseparately and the retrieval is done by finding related models. For retrievalof gene expression experiments, we use a probabilistic model called productpartition model, which induces a clustering of genes that show similarexpression patterns across a number of samples. The suggested metric forretrieval using clusterings is the normalized information distance. Empiricalresults finally suggest that inference for the full probabilistic model can beapproximated with good performance using computationally faster heuristicclustering approaches (e.g. $k$-means). The method is highly scalable andstraightforward to apply to construct a general-purpose gene expressionexperiment retrieval method.  Availability: The method can be implemented using standard clusteringalgorithms and normalized information distance, available in many statisticalsoftware packages.

Modelling-based experiment retrieval: A case study with gene expression clustering