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Popular online enrichment analysis tools from the field of molecular systemsbiology provide users with the ability to submit their experimental results asgene sets for individual analysis. Such queries are kept private, and havenever before been considered as a resource for integrative analysis. Byharnessing gene set query submissions from thousands of users, we aim todiscover biological knowledge beyond the scope of an individual study. In thiswork, we investigated a large collection of gene sets submitted to the toolEnrichr by thousands of users. Based on co-occurrence, we constructed a globalgene-gene association network. We interpret this inferred network as providinga summary of the structure present in this crowdsourced gene set library, andshow that this network recapitulates known protein-protein interactions andfunctional associations between genes. This finding implies that this networkalso offers predictive value. Furthermore, we visualize this gene-geneassociation network using a new edge-pruning algorithm that retains both thelocal and global structures of large-scale networks. Our ability to makepredictions for currently unknown gene associations, that may not be capturedby individual researchers and data sources, is a demonstration of the potentialof harnessing collective knowledge from users of popular tools in the field ofmolecular systems biology.

Large Collection of Diverse Gene Set Search Queries Recapitulate Known Protein-Protein Interactions and Gene-Gene Functional Associations