An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival
Abstract Background Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from...
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| Format: | Article |
| Language: | English |
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BMC
2018-07-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-018-0636-4 |
| _version_ | 1819014100388151296 |
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| author | Hansi Zhang Yi Guo Qian Li Thomas J. George Elizabeth Shenkman François Modave Jiang Bian |
| author_facet | Hansi Zhang Yi Guo Qian Li Thomas J. George Elizabeth Shenkman François Modave Jiang Bian |
| author_sort | Hansi Zhang |
| collection | DOAJ |
| description | Abstract Background Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. Methods Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs. Results Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. Conclusions Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA. |
| first_indexed | 2024-12-21T02:10:28Z |
| format | Article |
| id | doaj.art-19466a0e94b04b2b90a43aa06457525e |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-21T02:10:28Z |
| publishDate | 2018-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-19466a0e94b04b2b90a43aa06457525e2022-12-21T19:19:23ZengBMCBMC Medical Informatics and Decision Making1472-69472018-07-0118S212914710.1186/s12911-018-0636-4An ontology-guided semantic data integration framework to support integrative data analysis of cancer survivalHansi Zhang0Yi Guo1Qian Li2Thomas J. George3Elizabeth Shenkman4François Modave5Jiang Bian6Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of FloridaDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of FloridaDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of FloridaDivision of Hematology and Oncology, Department of Medicine, College of Medicine, University of FloridaDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of FloridaDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of FloridaDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of FloridaAbstract Background Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. Methods Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs. Results Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. Conclusions Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.http://link.springer.com/article/10.1186/s12911-018-0636-4Semantic data integrationOntologySemantic webCancer survivalIntegrative data analysis |
| spellingShingle | Hansi Zhang Yi Guo Qian Li Thomas J. George Elizabeth Shenkman François Modave Jiang Bian An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival BMC Medical Informatics and Decision Making Semantic data integration Ontology Semantic web Cancer survival Integrative data analysis |
| title | An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival |
| title_full | An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival |
| title_fullStr | An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival |
| title_full_unstemmed | An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival |
| title_short | An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival |
| title_sort | ontology guided semantic data integration framework to support integrative data analysis of cancer survival |
| topic | Semantic data integration Ontology Semantic web Cancer survival Integrative data analysis |
| url | http://link.springer.com/article/10.1186/s12911-018-0636-4 |
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