A web-based integration framework over heterogeneous biomedical data and knowledge sources
Abstract/Contents
- Abstract
- The biomedical research community has been one of the earliest adopters of Semantic Web technologies and Linked Data principles. Several databases and knowledge bases are published and linked on the Web using these technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. During this age of data and knowledge explosion in biomedicine, Semantic Web technologies and the LSLOD cloud may provide a unique opportunity toward the integration of disparate biomedical data and knowledge stored in isolated repositories. However, in the current state of the LSLOD cloud, it is still very difficult for most biomedical researchers to query and integrate data and knowledge from multiple sources simultaneously. The semantic heterogeneity across the LSLOD cloud makes the task of serendipitously discovering implicit associations illusive. I hypothesize that a Semantic Web pattern-based query federation framework can aid in the integration of multiple disparate, heterogeneous biomedical data and knowledge sources for discovering novel implicit associations in biomedicine serendipitously. I detect and quantify the manifestations of semantic heterogeneity across biomedical ontologies and linked data sources in the LSLOD cloud. I develop the PhLeGrA (Linked Graph Analytics in Pharmacology) framework for heterogeneous biomedical data and knowledge integration, and association discovery. I demonstrate the utility of the PhLeGrA framework to generate a systems pharmacology network composed of drugs, proteins, pathways and phenotypes. In conjunction with this systems pharmacology network, PhLeGrA mines clinical data in spontaneous reporting systems and electronic health records for detecting adverse drug reactions that manifest due to multiple drug intake, and provides explanations on the underlying biological mechanisms. The findings presented in this research will make Semantic Web developers and publishers more aware of the architectural issues associated with mining the LSLOD cloud. The methods that I have developed should enable biomedical researchers to query and integrate data and knowledge from multiple, heterogeneous LSLOD sources for solving complex biomedical problems.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Kamdar, Maulik Rajendra |
|---|---|
| Degree supervisor | Musen, Mark A |
| Thesis advisor | Musen, Mark A |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Polleres, Axel |
| Degree committee member | Altman, Russ |
| Degree committee member | Leskovec, Jurij |
| Degree committee member | Polleres, Axel |
| Associated with | Stanford University, Program in Biomedical Informatics. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Maulik R. Kamdar. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Maulik Rajendra Kamdar
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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