Linked Life Data

19953641

Source:http://linkedlifedata.com/resource/pubmed/id/19953641

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-12-24
pubmed:abstractText
RAMEDIS is a manually curated resource of human variations and corresponding phenotypes for rare metabolic diseases. The system is based on separate case reports that comprehensively describe various aspects of anonymous case study, e.g. molecular genetics, symptoms, lab findings, treatments, etc. Scientists are able to make use of the database by a simple and intuitive web-based user interface with a common web browser. A registration or login is not necessary for a full reading access to the system content. Furthermore, a mutation analysis table summarizes the submitted variations per diagnosis and enables direct access to detailed information of corresponding case reports. Interested scientists may open an account to submit their case reports in order to share valuable genotype-phenotype information efficiently with the scientific community. Currently, 794 case reports have been submitted, describing 92 different genetic metabolic diseases. To enhance the comprehensive coverage of available knowledge in the field of rare metabolic diseases, all case reports are linked to integrated information from public molecular biology databases like KEGG, OMIM and ENZYME. This information upgrades the case reports by related data of the corresponding diseases as well as involved enzymes, genes and metabolic pathways. Academic users may freely use the RAMEDIS system at http://www.ramedis.de.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1098-1004
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
E1081-8
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
RAMEDIS: a comprehensive information system for variations and corresponding phenotypes of rare metabolic diseases.
pubmed:affiliation
Bioinformatics Department, Bielefeld University, P.O. Box 10 01 31, D-33501 Bielefeld, Germany. horalf.toepel@uni-bielefeld.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't