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Linking life sciences data using graph-based mapping

Lookup NU author(s): Jochen Weile


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There are over 1100 different databases available containing primary and derived data of interest to research biologists. It is inevitable that many of these databases contain overlapping, related or conflicting information. Data integration methods are being developed to address these issues by providing a consolidated view over multiple databases. However, a key challenge for data integration is the identification of links between closely related entries in different life sciences databases when there is no direct information that provides a reliable cross-reference. Here we describe and evaluate three data integration methods to address this challenge in the context of a graph-based data integration framework (the ONDEX system). A key result presented in this paper is a quantitative evaluation of their performance in two different situations: the integration and analysis of different metabolic pathways resources and the mapping of equivalent elements between the Gene Ontology and a nomenclature describing enzyme function. © 2009 Springer Berlin Heidelberg.

Publication metadata

Author(s): Taubert J, Hindle M, Lysenko A, Weile J, Köhler J, Rawlings C

Editor(s): Paton, N.W., Missier, P., Hedeler, C.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Data Integration in the Life Sciences: 6th International Workshop

Year of Conference: 2009

Pages: 16-30

ISSN: 0302-9743 (Print) 1611-3349 (Online)

Publisher: Springer


DOI: 10.1007/978-3-642-02879-3_3

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783642028786