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Nucleic Acids Research 2005 33(Web Server Issue):W621-W626; doi:10.1093/nar/gki450
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© The Author 2005. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org


Article

ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics

Hee-Joon Chung1, Chan Hee Park1, Mi Ryung Han1, Seokho Lee1, Jung Hun Ohn1, Jihoon Kim1, Jihun Kim1 and Ju Han Kim1,2,*

1Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine Seoul 110-799, Korea 2Human Genome Research Institute, Seoul National University College of Medicine Seoul 110-799, Korea

*To whom correspondence should be addressed. Tel: +82 2 740 8320; Fax: +82 2 742 5947; Email: juhan{at}snu.ac.kr

Received February 13, 2005. Revised March 31, 2005. Accepted March 31, 2005.

Summary: ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics (SVG). Deciphering the crosstalk among pathways and integrating biomedical ontologies and knowledge bases may help biological interpretation of microarray data. ArrayXPath is empowered by integrating gene-pathway, disease-pathway, drug-pathway and pathway–pathway correlations with integrated Gene Ontology, Medical Subject Headings and OMIM Morbid Map-based annotations. We applied Fisher's exact test and relative risk to evaluate the statistical significance of the correlations. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of gene-expression profiles integrated with gene-pathway-disease interactions enriched by biomedical ontologies.


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