title: Beegle: from literature mining to disease-gene discovery.
abstract: Disease-gene identification is a challenging process that has multiple applications within functional genomics and personalized medicine. Typically, this process involves both finding genes known to be associated with the disease (through literature search) and carrying out preliminary experiments or screens (e.g. linkage or association studies, copy number analyses, expression profiling) to determine a set of promising candidates for experimental validation. This requires extensive time and monetary resources. We describe Beegle, an online search and discovery engine that attempts to simplify this process by automating the typical approaches. It 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. Beegle is publicly available at http://beegle.esat.kuleuven.be/.
authors.affiliation: Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, Leuven 3001, Belgium iMinds Future Health Department, KU Leuven, Leuven 3001, Belgium sarah.elshal@esat.kuleuven.be.
authors.forename: Sarah
authors.initials: S
authors.lastname: ElShal
authors.affiliation: Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, Leuven 3001, Belgium iMinds Future Health Department, KU Leuven, Leuven 3001, Belgium Inserm UMR-S1052, CNRS UMR5286, Cancer Research Centre of Lyon, Lyon, France Université de Lyon 1, Villeurbanne, France Centre Léon Bérard, Lyon, France.
authors.forename: Léon-Charles
authors.initials: LC
authors.lastname: Tranchevent
authors.affiliation: Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, Leuven 3001, Belgium iMinds Future Health Department, KU Leuven, Leuven 3001, Belgium Wellcome Trust Genome Campus, Hinxton, Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Sifrim
authors.affiliation: Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, Leuven 3001, Belgium iMinds Future Health Department, KU Leuven, Leuven 3001, Belgium.
authors.forename: Amin
authors.initials: A
authors.lastname: Ardeshirdavani
authors.affiliation: Department of Computer Science (DTAI), KU Leuven, Leuven 3001, Belgium.
authors.forename: Jesse
authors.initials: J
authors.lastname: Davis
authors.affiliation: Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, Leuven 3001, Belgium iMinds Future Health Department, KU Leuven, Leuven 3001, Belgium.
authors.forename: Yves
authors.initials: Y
authors.lastname: Moreau
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D006801:Humans; D011336:Probability; D057386:Search Engine; D012984:Software