Abstract
The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data including experimental data from “-omics” platforms, phenotype information, and clinical data. For bioinformatics, several challenges remain: to structure this information as biological networks enabling scientists to identify relevant information; to integrate this information as specific “knowledge bases”; and to formalize this knowledge across multiple scientific domains to facilitate hypothesis generation and validation and, thus, the generation of new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we will introduce techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks. The BioXM™ Knowledge Management Environment is used as an example to demonstrate how a domain such as oncology is represented and how this representation is utilized for research.
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Acknowledgments
The ideas and concepts outlined in this chapter have evolved over an extended period of time and have benefited from discussions with numerous friends and colleagues. The authors would especially like to thank Wenzel Kalus, without his work the BioXM system would not have become reality. The authors would also like to thank Sheridon Sauer for her very helpful assistance during the work on this manuscript.
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© 2009 Humana Press, a part of Springer Science+Business Media, LLC
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Losko, S., Heumann, K. (2009). Semantic Data Integration and Knowledge Management to Represent Biological Network Associations. In: Nikolsky, Y., Bryant, J. (eds) Protein Networks and Pathway Analysis. Methods in Molecular Biology, vol 563. Humana Press. https://doi.org/10.1007/978-1-60761-175-2_13
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DOI: https://doi.org/10.1007/978-1-60761-175-2_13
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