| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Papatheodorou, Irene Crichton, Charles Morris, Lorna Maccallum, Peter Davies, Jim Brenton, James D Caldas, Carlos |
| Abstract | Background In molecular profiling studies of cancer patients, experimental and clinical data are combined in order to understand the clinical heterogeneity of the disease: clinical information for each subject needs to be linked to tumour samples, macromolecules extracted, and experimental results. This may involve the integration of clinical data sets from several different sources: these data sets may employ different data definitions and some may be incomplete. Methods In this work we employ semantic web techniques developed within the CancerGrid project, in particular the use of metadata elements and logic-based inference to annotate heterogeneous clinical information, integrate and query it. Results We show how this integration can be achieved automatically, following the declaration of appropriate metadata elements for each clinical data set; we demonstrate the practicality of this approach through application to experimental results and clinical data from five hospitals in the UK and Canada, undertaken as part of the METABRIC project (Molecular Taxonomy of Breast Cancer International Consortium). Conclusion We describe a metadata approach for managing similarities and differences in clinical datasets in a standardized way that uses Common Data Elements (CDEs). We apply and evaluate the approach by integrating the five different clinical datasets of METABRIC. |
| Related Links | https://bmcmedgenomics.biomedcentral.com/counter/pdf/10.1186/1755-8794-2-66.pdf |
| Ending Page | 10 |
| Page Count | 10 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17558794 |
| DOI | 10.1186/1755-8794-2-66 |
| Journal | BMC Medical Genomics |
| Issue Number | 1 |
| Volume Number | 2 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2009-11-30 |
| Access Restriction | Open |
| Subject Keyword | Human Genetics Microarrays Gene Expression Inference Rule Resource Description Framework Semantic Annotation Molecular Taxonomy Clinical Dataset |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics (clinical) Genetics |
| Journal Impact Factor | 2.1/2023 |
| 5-Year Journal Impact Factor | 2.5/2023 |
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