Abstracting OWL for network inference
Tiffany J. Callahan, William A. Baumgartner, Marc Daya, Lawrence Hunter · 2019
Structural transformation of biological knowledge represented using Semantic Web standards significantly improves the utility of visualization tools and network analytics. Link prediction algorithms are powerful tools for predicting unobserved connections between nodes in a network. The application of such algorithms to biological networks has lead to the correct prediction of previously unobserved relationships ranging from protein-protein interactions to novel P53 kinases. The use of such algorithms to analyze larger and more complex representations has the potential to generate novel and important hypotheses, and insights into biological mechanisms. Unfortunately, the direct application of these algorithms to biological knowledge is limited by the representational complexity of the web ontology language standard OWL. The Network Information Content Entity (NICE) approach, a novel transformation method, reversibly transforms OWL-compliant biomedical knowledge into a representation better suited for visualization and network inference algorithms. Using several illustrative biomedical queries, the NICE transformation produces simpler network representations that are more visually comprehensible and whose structural properties (e.g. clustering coefficient, modularity, number of shortest paths, number of average neighbors, and diameter and radius) are significantly improved over raw OWL. Furthermore, comparison of the results from the application of several state-of-the-art link prediction algorithms on raw OWL versus NICE networks shows that the NICE transformation results in more accurate and biologically meaningful predictions. For each query and each algorithm, the top-ten predicted links for both OWL and NICE networks were validated via evidence from literature review and domain expert consultation.