![]() This information is presented in a common format whose semantics is described through the BioPAX ontology. įocusing on biological pathways, the Pathway Commons initiative readily provides access to information from resources such as KEGG, Reactome, HumanCyc and others on the Semantic Web. This is the case of the Ontology for Biomedical Investigation (OBI), that aims at providing terms for the annotation of protocols, instrumentation, materials and data. ![]() It should be noted that the development of ontologies in Life Sciences covers not only the domain of biomedical entities, but also methodologies. Within the same framework, languages for expressing queries such as SPARQL are also defined.Īn example of how heterogeneous information can be integrated and queried in this context has been developed by the W3C Healthcare and Life Sciences Interest Group. This de facto integrated set of resources can be queried not only for its content, but also for information that is a consequence of what is explicitly asserted, given the semantics provided by ontologies. In the Semantic Web vision, information resources such as biological databases can expose their data on the web in a common language (RDF ), together with ontologies encoding their semantics. Regarding the integration of heterogeneous information, ontology development in the Life Sciences is increasingly adopting the Semantic Web in particular through the OWL language. As a result, bio-ontologies are shifting from almost terminological resources used for annotation of biological entities, to a formal representation of a knowledge domain that allows inference of biologically meaningful facts. Current research is also focusing on clear and formal definition of entities, relations and their properties. The resulting ontologies provide a large shared terminology, but they have limited ontological commitment. The development of ontologies such as GO has been driven at first by the need of a wide-coverage annotation of the entities of their domain. A typical case of this use of GO is the functional characterization of patterns of gene expression data. GO is a unique resource for uniform annotation of gene products across organisms and it is used to relate experimental data and knowledge on processes and functions of genes on a high-throughput scale. The success of the Gene Ontology (GO) is an example of the usefulness of ontologies. Ontologies are necessary for the annotation and the interpretation of large datasets, for the integration of heterogeneous information and for the creation of common languages across disciplines, ranging from the Life Sciences to Healthcare. The role of ontologies in the Life Sciences domain has increased in recent years, as the development of high-throughput measurement technologies has made it a data-intensive discipline. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived. The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. New ontologies are being developed to formalize knowledge, e.g. The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain.
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