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Towards Bioinformatics Resourceomes

  • Chapter
Biomedical Data and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 224))

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Abstract

In science fiction, human beings have been depicted able to colonize planets of far stars exploiting their chemical and mining resources. We can image bioinformatics as a very dynamic universe, continuously growing under our eyes. More and more scientists are approaching it and would like to easier explore it, discovering the resources that can be found in every space region (i.e. related to every bioinformatics topic). We propose to intuitively organize into Resourceomes the hierarchical vision of a scientific domain perceived by its scientists, connecting the related resources to the topics they concern. A Resourceome can be seen as a map of a scientific “universe”. A semantic browser for Resourceomes permits to intuitively navigate in the domain, eventually zooming it in and out. Once discovered a resource of interest it is possible to be “tele-ported” to it. More importantly, the maps are “machine understandable”, being built on ontologies and published on the Web with Semantic Web technologies.

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Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri 9, 62032, Camerino (MC), Italy

    Nicola Cannata, Flavio Corradini, Emanuela Merelli, Francesca Piersigilli & Leonardo Vito

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  1. Nicola Cannata
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  2. Flavio Corradini
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  3. Emanuela Merelli
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  4. Francesca Piersigilli
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  5. Leonardo Vito
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Editors and Affiliations

  1. WA Centre for Comparative Genomics, Murdoch University, 6150, Murdoch, Western Australia, Australia

    Amandeep S. Sidhu

  2. Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, 6102, Park Bentley, WA, Australia

    Tharam S. Dillon

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Cannata, N., Corradini, F., Merelli, E., Piersigilli, F., Vito, L. (2009). Towards Bioinformatics Resourceomes. In: Sidhu, A.S., Dillon, T.S. (eds) Biomedical Data and Applications. Studies in Computational Intelligence, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02193-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-02193-0_2

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