title: Toward computational cumulative biology by combining models of biological datasets.
abstract: 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 include both 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 automatically 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 the keyword search; moreover, it 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.
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland.
authors.forename: Ali
authors.initials: A
authors.lastname: Faisal
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland.
authors.forename: Jaakko
authors.initials: J
authors.lastname: Peltonen
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Georgii
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom.
authors.forename: Johan
authors.initials: J
authors.lastname: Rung
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland; Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
authors.forename: Samuel
authors.initials: S
authors.lastname: Kaski
mesh_terms: D001271:Atlases as Topic; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D030541:Databases, Genetic / Q000706:statistics & numerical data*; D066264:Datasets as Topic; D015870:Gene Expression; D015894:Genome, Human; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods / Q000706:statistics & numerical data*