DDMGD: the database of text-mined associations between genes methylated in diseases from different species
KAUST DepartmentBioscience Core Lab
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
MetadataShow full item record
AbstractGathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.
CitationDDMGD: the database of text-mined associations between genes methylated in diseases from different species 2014 Nucleic Acids Research
SponsorsKing Abdullah University of Science and Technology.
PublisherOxford University Press (OUP)
JournalNucleic Acids Research
PubMed Central IDPMC4383966
The following license files are associated with this item:
- Combining position weight matrices and document-term matrix for efficient extraction of associations of methylated genes and diseases from free text.
- Authors: Bin Raies A, Mansour H, Incitti R, Bajic VB
- Issue date: 2013
- PubMeth: a cancer methylation database combining text-mining and expert annotation.
- Authors: Ongenaert M, Van Neste L, De Meyer T, Menschaert G, Bekaert S, Van Criekinge W
- Issue date: 2008 Jan
- Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.
- Authors: Bravo À, Piñero J, Queralt-Rosinach N, Rautschka M, Furlong LI
- Issue date: 2015 Feb 21
- DESTAF: a database of text-mined associations for reproductive toxins potentially affecting human fertility.
- Authors: Dawe AS, Radovanovic A, Kaur M, Sagar S, Seshadri SV, Schaefer U, Kamau AA, Christoffels A, Bajic VB
- Issue date: 2012 Jan
- DISEASES: text mining and data integration of disease-gene associations.
- Authors: Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ
- Issue date: 2015 Mar