Project Summary NIH Common Fund (CF) programs have produced a number of unique and high-value data sets. To solve complex biomedical questions, we need to find related data sets that can be co-analyzed for specific study purposes. Many of the current search techniques depend on data descriptors which differ across CF programs and may be incomplete or inaccurate. Many of these experiments output lists of genes significant to certain biomedical conditions. We are proposing to use these gene lists to find similar data sets. This approach will not only enable searching across CF data sets but also can connect them to other experiments in other databases and biomedical catalogs, e.g., databases containing disease-gene associations and molecular pathways. To achieve this aim, we will implement an efficient linear algorithm to calculate similarities between large numbers of gene sets. Our prototype tool, DBRetina, uses this algorithm to build huge similarity networks in few minutes using minimal computational resources. DBRetina serves as the foundation for CurIndex, a study similarity graph database that connects multiple health-related resources. DBRetina and CurIndex will allow advanced search for related CF experiments and facilitate better interpretation of biomedical data.

Project Narrative We will develop new data-driven search tools to allow advanced search for related Common Fund experiments based on data content rather than metadata annotations. Moreover, these tools will be able to provide new insights for meta-analysis by testing for the collective enrichment of related experimental profiles. The resulting enhanced discoverability of CF data sets will support the large scale meta-analysis studies needed to address complex biomedical questions.