DDMGD: the database of text-mined associations between genes methylated in diseases from different species

A. B. Raies, Hicham Mansour, Roberto Incitti, Vladimir B. Bajic

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Gathering 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.
Original languageEnglish (US)
Pages (from-to)D879-D886
Number of pages1
JournalNucleic Acids Research
Issue numberD1
StatePublished - Nov 14 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: King Abdullah University of Science and Technology.


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