title: Candidate gene prioritization with Endeavour.
abstract: Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using 'gold standard' gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene-phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.
authors.affiliation: INSERM U1210, CNRS UMR5239, Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, Université de Lyon, 69364 Lyon, France yves.moreau@esat.kuleuven.be.
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, B-3001 Leuven, Belgium iMinds Future Health Department, KU Leuven, B-3001 Leuven, Belgium.
authors.forename: Amin
authors.initials: A
authors.lastname: Ardeshirdavani
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, B-3001 Leuven, Belgium iMinds Future Health Department, KU Leuven, B-3001 Leuven, Belgium.
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, B-3001 Leuven, Belgium iMinds Future Health Department, KU Leuven, B-3001 Leuven, Belgium.
authors.forename: Daniel
authors.initials: D
authors.lastname: Alcaide
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, B-3001 Leuven, Belgium iMinds Future Health Department, KU Leuven, B-3001 Leuven, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Aerts
authors.affiliation: INSERM U1210, CNRS UMR5239, Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, Université de Lyon, 69364 Lyon, France.
authors.forename: Didier
authors.initials: D
authors.lastname: Auboeuf
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics Department, KU Leuven, B-3001 Leuven, Belgium iMinds Future Health Department, KU Leuven, B-3001 Leuven, Belgium.
authors.forename: Yves
authors.initials: Y
authors.lastname: Moreau
mesh_terms: D000465:Algorithms; D000818:Animals; D019985:Benchmarking; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D020407:Internet; D010641:Phenotype; D012984:Software