BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

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 disea...

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Detalles Bibliográficos
Autores principales: Liekens, Anthony ML, De Knijf, Jeroen, Daelemans, Walter, Goethals, Bart, De Rijk, Peter, Del-Favero, Jurgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218845/
https://www.ncbi.nlm.nih.gov/pubmed/21696594
http://dx.doi.org/10.1186/gb-2011-12-6-r57
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author Liekens, Anthony ML
De Knijf, Jeroen
Daelemans, Walter
Goethals, Bart
De Rijk, Peter
Del-Favero, Jurgen
author_facet Liekens, Anthony ML
De Knijf, Jeroen
Daelemans, Walter
Goethals, Bart
De Rijk, Peter
Del-Favero, Jurgen
author_sort Liekens, Anthony ML
collection PubMed
description 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.
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spelling pubmed-32188452011-11-18 BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation Liekens, Anthony ML De Knijf, Jeroen Daelemans, Walter Goethals, Bart De Rijk, Peter Del-Favero, Jurgen Genome Biol Software 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. BioMed Central 2011 2011-06-22 /pmc/articles/PMC3218845/ /pubmed/21696594 http://dx.doi.org/10.1186/gb-2011-12-6-r57 Text en Copyright ©2011 Liekens et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
spellingShingle Software
Liekens, Anthony ML
De Knijf, Jeroen
Daelemans, Walter
Goethals, Bart
De Rijk, Peter
Del-Favero, Jurgen
BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation
title BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation
title_full BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation
title_fullStr BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation
title_full_unstemmed BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation
title_short BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation
title_sort biograph: unsupervised biomedical knowledge discovery via automated hypothesis generation
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218845/
https://www.ncbi.nlm.nih.gov/pubmed/21696594
http://dx.doi.org/10.1186/gb-2011-12-6-r57
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