Application of Semantic Web Technology to Establish Knowledge Management and Discovery in the Life Sciences
MetadataShow full item record
- Institutt for biologi 
The last three decades has seen the successful development of many high-throughput technologies that have revolutionised and transformed biological research. The application of these technologies has generated large quantities of data allowing new approaches to analyze and integrate these data, which now constitute the field of Systems Biology. Systems Biology aims to enable a holistic understanding of a biological system by mapping interactions between all the biochemical components within the system. This requires integration of interdisciplinary data and knowledge to comprehensively explore the various biological processes of a system. Ontologies in biology (bio-ontologies) and the Semantic Web are playing an increasingly important role in the integration of data and knowledge by offering an explicit, unambiguous and rich representation mechanism. This increased influence led to the proposal of the Semantic Systems Biology paradigm to complement the techniques currently used in Systems Biology. Semantic Systems Biology provides a semantic description of the knowledge about the biological systems on the whole facilitating data integration, knowledge management, reasoning and querying. However, this approach is still a typical product of technology push, offering potential users access to the new technology. This doctoral thesis presents the work performed to bring Semantic Systems Biology closer to biological domain experts. The work covers a variety of aspects of Semantic Systems Biology: The Gene eXpression Knowledge Base is a resource that captures knowledge on gene expression. The knowledge base exploits the power of seamless data integration offered by the semantic web technologies to build large networks of varied datasets, capable of answering complex biological questions. The knowledge base is the result of the active collaboration with the Gastrin Systems Biology group here at the Norwegian University of Science and Technology. This resource was customised by the integration of additional data sets on users’ request. Additionally, the utility of the knowledge base is demonstrated by the conversion of biological questions into computable queries. The joint analysis of the query results has helped in filling knowledge gaps in the biological system of study. Biologists often use different bioinformatics tools to conduct complex biological analysis. However, using these tools frequently poses a steep learning curve for the life science researchers. Therefore, the thesis describes ONTO-ToolKit, a plug-in that allows biologists to exploit bio-ontology based analysis as part of biological workflows in Galaxy. ONTO-ToolKit allows users to perform ontology-based analysis to improve the depth of their overall analysis Visualisation plays a key role in aiding users understand and grasp the knowledge represented in bio-ontologies. To this end, OLSVis, a web application was developed to make ontology browsing intuitive and flexible. Finally, the steps needed to further advance the Semantic Systems Biology approach has been discussed.
Has partsAntezana, Erick; Venkatesan, Aravind; Mungall, Chris; Mironov, Vladimir; Kuiper, Martin. ONTO-ToolKit. BMC bioinformatics. (ISSN 1471-2105). 11 Suppl 12: S8, 2010. 10.1186/1471-2105-11-S12-S8. 21210987.
Blondé, Ward; Mironov, Vladimir; Venkatesan, Aravind; Antezana, Erick; De Baets, Bernard; Kuiper, Martin. Reasoning with bio-ontologies. Bioinformatics (Oxford, England). (ISSN 1367-4811). 27(11): 1562-8, 2011. 10.1093/bioinformatics/btr164. 21471019.
Venkatesan, Aravind; Mironov, Vladimir; Kuiper, Martin. Towards an integrated knowledge system for capturing gene expression events. 3rd International Conference on Biomedical Ontology (ICBO 2012), 2012.
Venkatesan, Aravind; Tripathi, S.; Galdeano, A. S.; Blonde, W.; Mironov, Vladimir; Lægrid, A; Kuiper, Martin. Network candidate discovery using the Gene eXpression Knowledge Base. .
Vercruysse, Steven; Venkatesan, Aravind; Kuiper, Martin. OLSVis. BMC bioinformatics. (ISSN 1471-2105). 13: 116, 2012. 10.1186/1471-2105-13-116. 22646023.