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The knowledge-driven exploration of integrated biomedical knowledge sources facilitates the generation of new hypotheses
Knowledge gained from the scientific literature can complement newly obtained experimental data in helping researchers understand the pathological processes underlying diseases. However, unless the scientific literature and experimental data are semantically integrated, it is generally difficult for scientists to exploit the two sources effectively. We argue that, in addition to the semantic integration of heterogeneous knowledge sources, the usability of the integrated resource by scientists is dependent upon the availability of knowledge visualization and exploration tools. Moreover, the integration techniques must be scalable and the exploration interfaces must be easy to use by bench scientists. The end goal of such integrated knowledge sources and exploration tools is to enable scientists to generate novel hypotheses from the knowledge they explore. We tested the feasibility of our approach on a real use case in the domain of human health and parasite biology. On the one hand, we integrated the experimental data generated as part of an on-going research on Chagas disease with the knowledge extracted from the PubMed articles, using Semantic Web technologies. On the other hand, we developed iExplore, a web tool with a graphical interface for interactive knowledge exploration, that allows non-technical users to explore the integrated knowledge base using a relationship-focused approach. We illustrate the effectiveness of our approach by describing the knowledge-driven process of using iExplore to generate a new hypothesis for the treatment of Chagas.