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The amount of bio-medical data available on the Web grows exponentially with time. The resulting large volume of data makes manual exploration very tedious. Moreover, the velocity at which this data changes and the variety of formats in which bio-medical data is published makes it difficult to access them in an integrated form. Finally, the lack of an integrated vocabulary makes querying this data more difficult. In this paper, we advocate the use of Linked Data to integrate, query and visualize bio-medical data. The resulting Big Linked Data allows discovering knowledge distributed across manifold sources, making it viable for the serendipitous discovery of novel knowledge. We present the concept of Big Linked Data by showing how the constant stream of new bio-medical publications can be integrated with the Linked Cancer Genome Atlas dataset (TCGA) within a virtual integration scenario. We ensure the scalability of our approach through the novel TopFed federated query engine, which we evaluate by comparing the query execution time of our system with that of FedX on Linked TCGA. Then, we show how we can harness the value hidden in the underlying integrated data by making it easier to explore through a user-friendly interface. We evaluate the usability of the interface by using the standard system usability questionnaire as well as a customized questionnaire designed for the users of our system. Our overall result of 77 suggests that our interface is easy to use and can thus lead to novel insights. [ABSTRACT FROM AUTHOR]

Abstract: The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to characterize several types of cancer. Datasets from biomedical domains such as TCGA present a particularly challenging task for those interested in dynamically aggregating its results because the data sources are typically both heterogeneous and distributed. The Linked Data best practices offer a solution to integrate and discover data with those characteristics, namely through exposure of data as Web services supporting SPARQL, the Resource Description Framework query language. Most SPARQL endpoints, however, cannot easily be queried by data experts. Furthermore, exposing experimental data as SPARQL endpoints remains a challenging task because, in most cases, data must first be converted to Resource Description Framework triples. In line with those requirements, we have developed an infrastructure to expose clinical, demographic and molecular data elements generated by TCGA as a SPARQL endpoint by assigning elements to entities of the Simple Sloppy Semantic Database (S3DB) management model. All components of the infrastructure are available as independent Representational State Transfer (REST) Web services to encourage reusability, and a simple interface was developed to automatically assemble SPARQL queries by navigating a representation of the TCGA domain. A key feature of the proposed solution that greatly facilitates assembly of SPARQL queries is the distinction between the TCGA domain descriptors and data elements. Furthermore, the use of the S3DB management model as a mediator enables queries to both public and protected data without the need for prior submission to a single data source. [ABSTRACT FROM AUTHOR]