title: Computational tools for prioritizing candidate genes: boosting disease gene discovery.
abstract: At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.
authors: Department of Electrical Engineering ESAT-SCD and IBBT-KU Leuven Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. yves.moreau@esat. kuleuven.be
authors: Yves
authors: Y
authors: Moreau
authors: Léon-Charles
authors: LC
authors: Tranchevent
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D056726:Genetic Association Studies / Q000379:methods* / Q000706:statistics & numerical data; D020022:Genetic Predisposition to Disease; D057895:Haploinsufficiency / Q000235:genetics; D006801:Humans; D051379:Mice; D008957:Models, Genetic