An intelligent system for signal transduction pathways and other higher order functional knowledge is presented. Molecular mechanisms of biological processes are typically represented as diagrams ("pathways") that have a graph-analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and range from metal ion to protein to biological processes in general. In addition, the kinds of interactions that connect biological entities are likewise diverse. Consequently, current knowledge about pathways is highly heterogeneous both in the sense of the types of constituents and the granularity of descriptions. To cope with this problem, the proposed system adopts a recursive and hierarchical representation model that enables the annotation and query of pathways or sub-pathways of arbitral granularity. By combining the use of this hierarchical structure and biological ontologies, literature-based information regarding biological mechanisms becomes accessible by computer.
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