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BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

Anthony ML Liekens1*, Jeroen De Knijf2, Walter Daelemans3, Bart Goethals2, Peter De Rijk1 and Jurgen Del-Favero1

Author Affiliations

1 Applied Molecular Genomics group, VIB Department of Molecular Genetics, Universiteit Antwerpen, Universiteitsplein 1, 2610 Wilrijk, Belgium

2 Advanced Database Research and Modelling group, Department of Mathematics and Computer Science, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium

3 Computational Linguistics and Psycholinguistics Research Center, Universiteit Antwerpen, Prinsstraat 13, 2000, Antwerpen, Belgium

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Genome Biology 2011, 12:R57  doi:10.1186/gb-2011-12-6-r57

Published: 22 June 2011


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 webcite.