Abstract
BackgroundBiological databases and pathway knowledgebases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledgebase as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference.ResultsWe design a series of logical tests to detect potential problems we might encounter using a particular knowledgebase, the Reactome database, with a particular computer-aided hypothesis evaluation tool, HyBrow. We develop an explicit formal language from the language implicit in the Reactome data format and specify a logic to evaluate models expressed using this language. We use the formalism of finite model theory in this work. We then use this logic to formulate tests for desirable properties (such as completeness, consistency, and well-formedness) for pathways stored in Reactome.We apply these tests to the publicly available Reactome releases (releases 10 through 14) and compare the results, which highlight Reactome's steady improvement in terms of decreasing inconsistencies. We also investigate and discuss Reactome's potential for supporting computer-aided inference tools.ConclusionThe case study described in this work demonstrates that it is possible to use our model theory based approach to identify problems one might encounter using a knowledgebase to support hypothesis evaluation tools. The methodology we use is general and is in no way restricted to the specific knowledgebase employed in this case study. Future application of this methodology will enable us to compare pathway resources with respect to the generic properties such resources will need to possess if they are to support automated reasoning.
Highlights
Biological databases and pathway knowledgebases are proliferating rapidly
The case study described in this work demonstrates that it is possible to use our model theory based approach to identify problems one might encounter using a knowledgebase to support hypothesis evaluation tools
The methodology we use is general and is in no way restricted to the specific knowledgebase employed in this case study
Summary
Biological databases and pathway knowledgebases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. Knowledgebases which represent biological pathways in an abstracted, structured manner are being developed on a large scale [2,3,4]. Methods have been developed to compare the differential content in metabolic databases, using Petri nets to capture pathway representations from different databases[8] This paper complements such efforts and presents methods to evaluate the ability of a pathway knowledgebase to support computer-aided information integration and inference tools[9]. Another key difference is that this work provides a general methodology for proofreading knowledgebases, one based upon the formalism of model checking
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