An online search and discovery engine that attempts to simplify disease-gene identification by automating the typical approaches. Beegle starts by mining the literature to quickly extract a set of genes known to be linked with a given query, then it integrates the learning methodology of Endeavour (a gene prioritization tool) to train a genomic model and rank a set of candidate genes to generate novel hypotheses. In a realistic evaluation setup, Beegle has an average recall of 84% in the top 100 returned genes as a search engine, which improves the discovery engine by 12.6% in the top 5% prioritized genes.
Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, Leuven, Belgium
Beegle funding source(s)
Research Council KU Leuven [CoE PFV/10/016 SymBioSys and OT/11/051]; Innovation by Science and Technology; Industrial Research fund; Hercules Stichting; iMinds Medical Information Technologies [SBO 2015]; EU FP7 Marie Curie Career Integration Grant ; FWO-Vlaanderen [G.0356.12]; IMEC mandaat [Ph.D. mandaat]