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

Handle URI:
http://hdl.handle.net/10754/336990
Title:
DDMGD: the database of text-mined associations between genes methylated in diseases from different species
Authors:
Raies, A. B.; Mansour, H.; Incitti, R.; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Abstract:
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.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Biosciences Core Lab
Citation:
DDMGD: the database of text-mined associations between genes methylated in diseases from different species 2014 Nucleic Acids Research
Publisher:
Oxford University Press
Journal:
Nucleic Acids Research
Issue Date:
14-Nov-2014
DOI:
10.1093/nar/gku1168
PubMed ID:
25398897
PubMed Central ID:
PMC4383966
Type:
Article
ISSN:
0305-1048; 1362-4962
Sponsors:
King Abdullah University of Science and Technology.
Additional Links:
http://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gku1168
Appears in Collections:
Articles; Biosciences Core Lab; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorRaies, A. B.en
dc.contributor.authorMansour, H.en
dc.contributor.authorIncitti, R.en
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2014-12-09T14:04:54Z-
dc.date.available2014-12-09T14:04:54Z-
dc.date.issued2014-11-14en
dc.identifier.citationDDMGD: the database of text-mined associations between genes methylated in diseases from different species 2014 Nucleic Acids Researchen
dc.identifier.issn0305-1048en
dc.identifier.issn1362-4962en
dc.identifier.pmid25398897en
dc.identifier.doi10.1093/nar/gku1168en
dc.identifier.urihttp://hdl.handle.net/10754/336990en
dc.description.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.en
dc.description.sponsorshipKing Abdullah University of Science and Technology.en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.relation.urlhttp://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gku1168en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
dc.titleDDMGD: the database of text-mined associations between genes methylated in diseases from different speciesen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentBiosciences Core Laben
dc.identifier.journalNucleic Acids Researchen
dc.identifier.pmcidPMC4383966en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorMansour, Hichamen
kaust.authorIncitti, Robertoen
kaust.authorBajic, Vladimir B.en
kaust.authorRaies, Arwa Binen

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