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Cancer is still one of the leading causes of death in the developed world. To address this problem, the US National Institutes of Health (NIH) has launched The Cancer Genome Atlas (TCGA), a large scale systematic approach to characterize tumor samples from 20 cancer types and approximately 10,000 donor patients by using multiple high-throughput approaches.Multidisciplinary projects such as TCGA and related projects across Europe aim at identifying cancer “driver” mutations to be used as therapeutic targets or diagnostic tests. Extracting and aggregating the knowledge necessary to identify such mutations remains a challenge primarily due to the need to reliably integrate the experimental datasets with heterogeneous, distributed biomedical data sources, including public databases, biomedical literature and controlled access clinical information.Linked Data is a set of best practices to integrate and link data with those characteristics. We present here a method to easily integrate and enrich high-throughput experiment results such as those generated by TCGA, with public biomedical data sources such as Diseasome, DrugBank or KEGG, available in Linked Data formats. |
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