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Entrez Neuron: an OWL/RDFa–based Web Application for Information Exploration and Integration in Neuroscience. Matthias Samwald, Ernest Lim, Peter Masiar, Luis Marenco, Huajun Chen , Thomas Morse, Gordon Shepherd, Perry Miller and Kei Cheung Yale Center for Medical informatics, Yale University

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  1. Entrez Neuron: an OWL/RDFa–based Web Application for Information Exploration and Integration in Neuroscience Matthias Samwald, Ernest Lim, Peter Masiar, Luis Marenco, Huajun Chen, Thomas Morse, Gordon Shepherd, Perry Miller and Kei Cheung Yale Center for Medical informatics, Yale University Medical University of Vienna, Vienna, Austria School of Computer Science, Zhejiang University

  2. Outline • SenseLab Overview • Entrez Neuron Project • Semantic Data Conversion • Semantic Data Browsing/Querying • Future Development • Online demo

  3. Yale SenseLab Overview SenseLab’s development was funded by the Human Brain Project to disseminate information related to neuronal membrane properties with special emphasis on the olfactory system. • A major information resource for international neuroscience research. • Have garnered several million hits and unique visits over the years.

  4. SenseLab Database • SenseLab currently contains eight databases: • These databases combine to provide neuronal, genomics/genetics, proteomics and imaging information to the neuroscience research community and the public. • These resources have been used in neuroscience research, teaching, thesis-writing and database design, and Semantic Web exploration.

  5. Snapshot of SenseLab Databases http://senselab.med.yale.edu/

  6. Entrez Neuron Project A neuron-centric interface that allows for keyword-based queries against a coherent repository of OWL ontologies about neuronal structure, physiology, mathematical models and microscopy.

  7. Technical Approach • Careful modeling of neuroscience domain knowledge in consideration of reusing many well-defined general purpose biomedical ontologies. • Convert legacy Senselab data to desired RDF/OWL model. • Map out to external biomedical resources. • Provide integrated ontological browse, search services over the knowledge base. • Use RDFa to publish the knowledge as HTML.

  8. Ontology modeling • Basic ontology: an ontology containing basic class hierarchies and relations was manually created, based on the structure of existing SenseLab databases. • The ontologies were built upon established foundational ontologies in order to maximize the interoperability with other existing and forthcoming biomedical Semantic Web resources. These ontologies were: • the Relation Ontology [RO] from the Open Biomedical Ontologies repository [OBO], which defines basic relations such as 'part of', 'participant of' or 'contained in'. • the Basic Formal Ontology [BFO], which defines basic classes such as 'process', 'object', 'quality' or 'function'.

  9. Data conversion • Based on this manually created basic ontology, the data from the SenseLab databases were then automatically converted to OWL using programs written in Java and Python. • The automated export scripts extends the manually created basic ontology through the creation of subclasses, OWL property restrictions and individuals.

  10. Data Conversion: Mapping Out • Mappings were made to the following ontologies: • the BAMS ontology which was derived from the Brain Architecture Management System [BAMS] • the Subcellular Anatomy Ontology (SAO) created by the Cell Centered Database project. [SAO] • the BirnLex ontology developed by members of the Biomedical Informatics Research Network [BIRNLEX] • the Common Anatomy Reference Ontology (CARO) [CARO] • the Gene Ontology [GO] • the Ontology of Biomedical Investigation (OBI) [OBI]

  11. Data Conversion: Mapping Out

  12. Data Conversion: Mapping Out

  13. Outcome of SenseLab conversions

  14. Example Queries W3C note on http://www.w3.org/TR/hcls-senselab/

  15. But how can end-users profit? Lack of user-friendly interfacesSpectrum of Semantic Web user interfaces with two extremes: • Very flexible, but very generic and therefore not user friendly • Very specific and user-friendly, but only geared towards a small set of ontological entities Practical user interfaces probably need to be positioned somewhere in the middle

  16. Design the User Interface

  17. Key Features • Facet Search enabled by ontology description. • Use RDFa to encode the semantic information to HTML

  18. The Entrez Neuron Project

  19. Future developments • Adding more RDF/OWL data • PDSP Ki database (ligand-receptor interactions) • SWAN ontology (Alzheimer's disease hypotheses) • Further OBO ontologies • Querying and Reasoning • Switch to using SPARQL (instead of Oracle RDF query language) • Explore and expand reasoning capabilities

  20. Future developments • User interface • Add more features to the existing user interface • 3D Visualization • Community development • Establish Entrez Neuron as an open community platform

  21. Acknowledgement • Cell Centered Database: Maryann Martone, Willy Wong • Science Commons: Alan Ruttenberg • Oracle: Melliyal Annamalai, Alan Wu

  22. Thanks for your Attention

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