A Framework for Mining Life Sciences Data on the Semantic Web in an Interactive, Graph-Based Environment
| Autor: | Matthew Hindle, Andrea Splendiani, Christopher J. Rawlings, J Grzebyta, Artem Lysenko |
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| Přispěvatelé: | Formenti, E., Wit, E., Tagliaferri, R. |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Computational Intelligence Methods for Bioinformatics and Biostatistics ISBN: 9783319090412 CIBB |
| DOI: | 10.1007/978-3-319-09042-9_16 |
| Popis: | The last decade saw the marked increase in the availability of the Life Sciences data on the Semantic Web. At the same time, the need to interactively explore complex and extensive biological datasets lead to development of advanced visualisation tools, many of which present the data in the form of a network graph. Semantic Web technologies offer both a means to define rich semantics necessary to describe complex biological systems and allow large amounts of data to be shared effectively. However, at present the need to be familiar with relevant technologies greatly impedes access to these datasets by the non-specialist Life Sciences researches. To address this, we have developed a software frame-work that facilitates both access to the resources and presents the data returned in an intuitive, graph-based format. Our framework is closely integrated with Ondex, an established data integration solution in the Life Sciences domain. The implementation consists of two parts. The first one is a query console that allows expert users to execute Semantic Web queries directly. The second one is a graph-based interactive browsing solution that can be used to launch stock queries by choosing items in the menu. In both cases, the result is re-formatted and visualised as a graph in Ondex frontend. |
| Databáze: | OpenAIRE |
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