An Approach for Semantic Data Integration in Cancer Studies

Contemporary development in personalized medicine based both on extended clinical records and implementation of different high-throughput "omics" technologies has generated large amounts of data. To make use of these data, new approaches need to be developed for their search, storage, analysis, integration and processing. In this paper we suggest an approach for integration of data from diverse domains and various information sources enabling extraction of novel knowledge in cancer studies. Its application can contribute to the early detection and diagnosis of cancer as well as to its proper personalized treatment. The data used in our research consist of clinical records from two particular cancer studies with different factors and different origin, and also include gene expression datasets from different high-throughput technologies - microarray and next generation sequencing. An especially developed workflow, able to deal effectively with the heterogeneity of data and the enormous number of relations between patients and proteins, is used to automate the data integration process. During this process, our software tool performs advanced search for additional expressed protein relationships in a set of available knowledge sources and generates semantic links to them. As a result, a set of hidden common expressed protein mutations and their subsequent relations with patients is generated in the form of new knowledge about the studied cancer cases.