The project develops new techniques for the interactive navigtion, visualization, and analysis of heterogeneous biological networks

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The objective of this project is the development of novel visual analytics techniques for biological data in the context of networks. The core components of the visual analytics framework are automated methods for data integration, efficient data analysis methods, and sophisticated graph drawing techniques. Data integration will be strongly supported and simplified by using an extended BioPAX model. The novel visual analytics techniques will enable users to map high-throughput data (transcriptomics, proteomics, metabolomics) onto the unified network data of several public databases and to carry out challenging analyses in an efficient and effective way. Customized data visual analytics tools for various clinical applications (e.g., personalized cancer therapy) will enable the efficient analysis of these high-throughput data sets in medical settings. In contrast to the first funding period, where the focus was on laying the foundations of a visual analytics framework for biological networks, the second period will concentrate on bringing this framework closer to the application. Together with selected collaboration partners we will literally bring visual analytics to the physician’s desk.