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dc.contributor.author Wild, David J.
dc.contributor.author Desai, Pankaj
dc.contributor.author Qiu, Judy
dc.contributor.author Moorthy, Ganesh
dc.contributor.author Chen, Bin
dc.contributor.author Shin, Jae Hong
dc.contributor.author Sun, Yuyin
dc.contributor.author Wang, Huijun
dc.contributor.author Ding, Ying
dc.contributor.author Tang, Jie
dc.contributor.author He, Bing
dc.date.accessioned 2012-04-09T17:46:04Z
dc.date.available 2012-04-09T17:46:04Z
dc.date.issued 2011-12-06
dc.identifier.citation He, B., Tang, J., Ding, Y., Wang, H., Sun, Y., Shin, J. H., . . . Wild, D. J. (2011). Mining relational paths in integrated biomedical data. PLoS ONE, 6(12). http://dx.doi.org/10.1371/journal.pone.0027506 en
dc.identifier.uri http://dx.doi.org/10.1371/journal.pone.0027506 en
dc.identifier.uri http://hdl.handle.net/2022/14351
dc.description.abstract Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies. en
dc.language.iso en_US en
dc.publisher Public Library of Science en
dc.rights Copyright 2011, He et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. en
dc.title Mining relational paths in integrated biomedical data en
dc.type Article en


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