title: BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation.
abstract: We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be.
authors.affiliation: Applied Molecular Genomics group, VIB Department of Molecular Genetics, Universiteit Antwerpen, Universiteitsplein 1, Wilrijk, Belgium. anthony@liekens.net
authors.forename: Anthony M L
authors.initials: AM
authors.lastname: Liekens
authors.forename: Jeroen
authors.initials: J
authors.lastname: De Knijf
authors.forename: Walter
authors.initials: W
authors.lastname: Daelemans
authors.forename: Bart
authors.initials: B
authors.lastname: Goethals
authors.forename: Peter
authors.initials: P
authors.lastname: De Rijk
authors.forename: Jurgen
authors.initials: J
authors.lastname: Del-Favero
mesh_terms: D019295:Computational Biology; D057225:Data Mining; D016208:Databases, Factual; D055106:Genome-Wide Association Study; D006801:Humans; D012559:Schizophrenia / Q000235:genetics; D012984:Software; D018511:Systems Integration