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Paper ID: 1681

Using Text Mining for Understanding Insulin Signalling
Moustafa,Ghanem John,Ratcliffe Xinzhong,Li Vasa,Curcin Yike,Guo Roger,Tatoud James,Scott

Appeared in: Proceedings of the UK e-Science All Hands Conference 2005 website: http://www.allhands.org.uk/2005/
Page Numbers:
Publisher: Engineering and Physical Sciences Research Council
Year: 2005
ISBN/ISSN: 1-904425-53-4
Contributing Organisation(s):
Field of Science: e-Science

URL: http://www.allhands.org.uk/2005/proceedings/papers/458.pdf

Abstract: In this paper we describe our efforts and experience in using a mix of e-Science and text mining technologies in the context of large scale integrative biology studies. Using insulin signaling as an application framework, we describe the service-based text mining infrastructure used for the project and present a number of text mining workflows for performing a number of common tasks encountered in integrative biology studies, including document categorization, and literature-based methods for the identification of gene-tissue interactions, for performing automated gene differentiation and scoring, and for automatically labeling gene groups.

Keywords: e-Science, AHM 2005


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Last Updated: 22 Jun 12 11:02
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