  A main challenge of data-driven sciences is how to make maximal use of the
progressively expanding databases of experimental datasets in order to keep
research cumulative. We introduce the idea of a modeling-based dataset
retrieval engine designed for relating a researcher's experimental dataset to
earlier work in the field. The search is (i) data-driven to enable new
findings, going beyond the state of the art of keyword searches in annotations,
(ii) modeling-driven, to both include biological knowledge and insights learned
from data, and (iii) scalable, as it is accomplished without building one
unified grand model of all data. Assuming each dataset has been modeled
beforehand, by the researchers or by database managers, we apply a rapidly
computable and optimizable combination model to decompose a new dataset into
contributions from earlier relevant models. By using the data-driven
decomposition we identify a network of interrelated datasets from a large
annotated human gene expression atlas. While tissue type and disease were major
driving forces for determining relevant datasets, the found relationships were
richer and the model-based search was more accurate than keyword search; it
moreover recovered biologically meaningful relationships that are not
straightforwardly visible from annotations, for instance, between cells in
different developmental stages such as thymocytes and T-cells. Data-driven
links and citations matched to a large extent; the data-driven links even
uncovered corrections to the publication data, as two of the most linked
datasets were not highly cited and turned out to have wrong publication entries
in the database.
