HyQue (for Hypothesis-based Querying of pathway models), will take as input working hypotheses about pathway models expressed in a knowledge-based formalism, evaluate their consistency using existing data in a knowledgebase, and provide as output contradictory evidence and suggestions for improving hypotheses. HyQue will incorporate formal knowledge representations based upon Semantic Web standards and an ontology to represent biological objects and relationships.
The heart of this project is the development and prototyping of a new paradigm for the query and integration of diverse biological data. Information is integrated at the logical level, so that adding new information and data types is straightforward and so that each piece of data contributes to the evaluation. Our reasoning procedures are contradiction based, so that each data item rules out a subset of all hypotheses that can be constructed. Although we plan to experiment with SGD, the underlying methods for the ontology, evaluation rule library, interface and archive can be adapted to other organisms.
This research explores a unique and promising avenue of querying data to convert information into the task-specific knowledge needed by biologists. By offering a combination of a high level of generality with a sound, scalable reasoning framework, our proposed methods and tools will allow the iterative refinement of working hypotheses in ways that currently are not possible.
HyQue is based on our previous work on HyBrow.