Abstract
BackgroundKey to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks.ResultsWe present HyQue, a Semantic Web tool for querying scientific knowledge bases with the purpose of evaluating user submitted hypotheses. HyQue features a knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated against experimental and literature-sourced evidence through a combination of SPARQL queries and evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. We evaluate hypotheses of varying levels of detail about the genetic network controlling galactose metabolism in Saccharomyces cerevisiae to demonstrate the feasibility of deploying such semantic computing tools over a growing body of structured knowledge in Bio2RDF.ConclusionsHyQue is a query-based hypothesis evaluation system that can currently evaluate hypotheses about the galactose metabolism in S. cerevisiae. Hypotheses as well as the supporting or refuting data are represented in RDF and directly linked to one another allowing scientists to browse from data to hypothesis and vice versa. HyQue hypotheses and data are available at http://semanticscience.org/projects/hyque.
Highlights
Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data
Advancing knowledge in the biological sciences involves experimentally testing hypotheses and interpreting the results based on prior scientific work; as a result, research biologists must carry out the intensive tasks of collecting, evaluating and integrating large amounts of different kinds of information about organisms, cells, genes and proteins to generate a hypothesis about relationships that exist in the biological system under study
Hypotheses as well as the supporting or refuting data are represented in Resource Description Framework (RDF) and directly linked to one another allowing browsing from data to hypothesis and vice versa
Summary
Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks. There is a shortage of tools and methods that can handle the task of integrating this information and allow a scientist to draw meaningful inferences. Advancing knowledge in the biological sciences involves experimentally testing hypotheses and interpreting the results based on prior scientific work; as a result, research biologists must carry out the intensive tasks of collecting, evaluating and integrating large amounts of different kinds of information about organisms, cells, genes and proteins to generate a hypothesis about relationships that exist in the biological system under study. Researchers face the challenge of seeking out new, relevant information online along with managing and interpreting volumes of experimental data
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