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Skeletome Overview

The Skeletome project is a three-year research collaboration funded through an Australian Research Council (ARC) Linkage grant (LP100100156) between:

  • The University of Queensland (School of Information Technology and Electrical Engineering);
  • The Royal Brisbane and Women’s Hospital (RBWH).
Skeletal dysplasias are a heterogeneous group of genetic disorders affecting skeletal development. They affect up to 10,000 children and adults in Australia and 4,000,000 patients worldwide suffer from one of these debilitating disorders. Molecular genetic research on skeletal dysplasias has advanced considerably over the years. However, the lack of a comprehensive information repository about skeletal dysplasias hinders collaborative research and diagnosis in this area.

In recent years a number of emerging technologies such as the Semantic Web , Web 2.0 and social networking have gained significant popularity and momentum. Semantic Web technologies (such as RDF and OWL ontologies) provide machine-processable methods for describing and relating online resources, facilitating automated information processing, aggregation and integration. Web 2.0 and social networking techniques enable community participation and encourage users to add value to online collections through social tagging, collaborative content development (Wikis, podcasts) and online commentary (blogs). Together these technologies provide a powerful toolset for creating structured, interactive, dynamic and information-rich Web Portals. They also provide the ideal infrastructure for the ongoing, collaborative development, update and dissemination of a Skeletal Dysplasia Nosology.

Project Goals

The specific goals of the Skeletome project are to develop the following ICT infrastructure to support an international network of patients, clinicians and researchers seeking a better understanding of skeletal dysplasias:

  • A community-generated online knowledgebase that will significantly improve diagnosis, treatment, management and understanding of skeletal dysplasias and related disorders such as osteoporosis and osteoarthritis;
  • An ontology-enabled Wiki for collaborative authoring of case studies and automated knowledge acquisition and management;
  • Ontologies for integrating information about skeletal dysplasias, including molecular genetic causes, clinical features, X-ray imagery, diagnosis and management information;
  • Semantic annotation, inferencing and querying services for relevant mixed-media content;
  • Social-networking analysis tools for mapping domain expertise;
  • Automatic text processing techniques for knowledge extraction and semantic tagging.

The high-level architecture of the Skeletome knowledge base.