Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases

BMC Bioinformatics

Research

Hyunjung Shin¹  Young Soo Song²  Hee-Joon Chung³  Ju Han Kim³  Chan Hee Park³  Jihun Kim³ 

[1] Department of Industrial & Information Systems Engineering, Ajou University, 443-749, Suwon, Korea;Department of Industrial & Information Systems Engineering, Ajou University, 443-749, Suwon, Korea;Seoul National University Biomedical Informatics (SNUBI), Div. of Biomedical Informatics, Seoul National University College of Medicine, 110-799, Seoul, Korea;Systems Biomedical Informatics Research Center, Seoul National University, 110799, Seoul, Korea;Seoul National University Biomedical Informatics (SNUBI), Div. of Biomedical Informatics, Seoul National University College of Medicine, 110-799, Seoul, Korea;Systems Biomedical Informatics Research Center, Seoul National University, 110799, Seoul, Korea;

关键词: Resource Description Framework;    Biological Database;    Hypothesis Model;    Resource Description Framework Triple;    Resource Description Framework Representation;   

DOI  :  10.1186/1471-2105-12-S1-S51

来源: Springer

【 摘 要 】

BackgroundAlthough many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions.MethodsAn application-specific ontology was developed for managing TMA and DNA microarray databases by semantic web technologies. Data were represented as Resource Description Framework (RDF) according to the framework of the ontology. Applications for hypothesis testing (Xperanto-RDF) for TMA data were designed and implemented by (1) formulating the syntactic and semantic structures of the hypotheses derived from TMA experiments, (2) formulating SPARQLs to reflect the semantic structures of the hypotheses, and (3) performing statistical test with the result sets returned by the SPARQLs.ResultsWhen a user designs a hypothesis in Xperanto-RDF and submits it, the hypothesis can be tested against TMA experimental data stored in Xperanto-RDF. When we evaluated four previously validated hypotheses as an illustration, all the hypotheses were supported by Xperanto-RDF.ConclusionsWe demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited.

【 授权许可】

Unknown   
© Song et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.