Title
BioGraph : unsupervised biomedical knowledge discovery via automated hypothesis generation BioGraph : unsupervised biomedical knowledge discovery via automated hypothesis generation
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Faculty of Arts. Linguistics and Literature
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
London ,
Subject
Biology
Human medicine
Computer. Automation
Source (journal)
Genome biology. - London
Volume/pages
12(2011) :6 , p. R57,1-R57,12
ISSN
1465-6906
ISI
000296646600005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/1fbae8/1f0fb293.pdf
E-info
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