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Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases
BACKGROUND: Although 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...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044309/ https://www.ncbi.nlm.nih.gov/pubmed/21342584 http://dx.doi.org/10.1186/1471-2105-12-S1-S51 |
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author | Song, Young Soo Park, Chan Hee Chung, Hee-Joon Shin, Hyunjung Kim, Jihun Kim, Ju Han |
author_facet | Song, Young Soo Park, Chan Hee Chung, Hee-Joon Shin, Hyunjung Kim, Jihun Kim, Ju Han |
author_sort | Song, Young Soo |
collection | PubMed |
description | BACKGROUND: Although 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. METHODS: An 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. RESULTS: When 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. CONCLUSIONS: We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited. |
format | Text |
id | pubmed-3044309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30443092011-02-25 Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases Song, Young Soo Park, Chan Hee Chung, Hee-Joon Shin, Hyunjung Kim, Jihun Kim, Ju Han BMC Bioinformatics Research BACKGROUND: Although 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. METHODS: An 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. RESULTS: When 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. CONCLUSIONS: We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited. BioMed Central 2011-02-15 /pmc/articles/PMC3044309/ /pubmed/21342584 http://dx.doi.org/10.1186/1471-2105-12-S1-S51 Text en Copyright ©2011 Song et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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. |
spellingShingle | Research Song, Young Soo Park, Chan Hee Chung, Hee-Joon Shin, Hyunjung Kim, Jihun Kim, Ju Han Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases |
title | Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases |
title_full | Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases |
title_fullStr | Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases |
title_full_unstemmed | Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases |
title_short | Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases |
title_sort | semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044309/ https://www.ncbi.nlm.nih.gov/pubmed/21342584 http://dx.doi.org/10.1186/1471-2105-12-S1-S51 |
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