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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 5174503, 10 pages
Research Article

Semantic Signature: Comparative Interpretation of Gene Expression on a Semantic Space

Jihun Kim,1,2 Keewon Kim,1,3,4 and Ju Han Kim1,5

1Seoul National University Biomedical Informatics (SNUBI), Seoul 110-799, Republic of Korea
2LabGenomics Clinical Research Institute, LabGenomics, Seongnam 463-400, Republic of Korea
3Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul 110-799, Republic of Korea
4Departments of Biomedical Engineering, Seoul National University College of Medicine, Seoul 110-799, Republic of Korea
5Systems Biomedical Informatics Research Center, Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110-799, Republic of Korea

Received 17 December 2015; Accepted 23 March 2016

Academic Editor: Seiya Imoto

Copyright © 2016 Jihun Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Interpretation of microarray data remains challenging because biological meaning should be extracted from enormous numeric matrices and be presented explicitly. Moreover, huge public repositories of microarray dataset are ready to be exploited for comparative analysis. This study aimed to provide a platform where essential implication of a microarray experiment could be visually expressed and various microarray datasets could be intuitively compared. Results. On the semantic space, gene sets from Molecular Signature Database (MSigDB) were plotted as landmarks and their relative distances were calculated by Lin’s semantic similarity measure. By formal concept analysis, a microarray dataset was transformed into a concept lattice with gene clusters as objects and Gene Ontology terms as attributes. Concepts of a lattice were located on the semantic space reflecting semantic distance from landmarks and edges between concepts were drawn; consequently, a specific geographic pattern could be observed from a microarray dataset. We termed a distinctive geography shared by microarray datasets of the same category as “semantic signature.” Conclusions. “Semantic space,” a map of biological entities, could serve as a universal platform for comparative microarray analysis. When microarray data were displayed on the semantic space as concept lattices, “semantic signature,” characteristic geography for a microarray experiment, could be discovered.