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, originating from a multitude of “-omics” platforms, phenotype information, and clinical data. For bioinformatics, the challenge remains to structure this information so that scientists can 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. Here we report on progress made in building a generic knowledge management environment capable of representing and mining both explicit and implicit knowledge and, thus, generating new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we 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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References
Searls DB (2005) Data integration: challenges for drug discovery. Nat Rev Drug Discov 4:45–58
Mukherjea S (2005) Information retrieval and knowledge discovery utilising a biomedical Semantic Web. Brief Bioinform 6:252–262
Kashyap V (2003) The UMLS Semantic Network and the Semantic Web. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium, pp 351–355
Losko S et al (2006) Knowledge networks of biological and medical data: an exhaustive and flexible solution to model life science domains. In: Data integration in the life sciences, Lecture notes in computer science, vol 4075. Springer, New York, NY, pp 232–239
Settles B (2005) ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics 21:3191–3192
Rocktäschel T, Weidlich M, Leser U (2012) ChemSpot: a hybrid system for chemical named entity recognition. Bioinformatics 28:1633–1640
Kaps A et al (2006) The BioRS™ Integration and retrieval system: an open system for distributed data integration. J Integr Bioinform 3
Stark C et al (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539
Piñero J et al (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015:bav028–bav028
GTEx Consortium (2015) Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348:648–660
Gene Ontology Consortium (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res 43:D1049–D1056
Sioutos N et al (2007) NCI Thesaurus: a semantic model integrating cancer-related clinical and molecular information. J Biomed Inform 40:30–43
Kibbe WA et al (2015) Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43:D1071–D1078
Fowler M, Highsmith J (2001) The agile manifesto. Software Dev 9(8):28–32
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 and Martin Wolff. Without their work the BioXM system would not have become a reality in its current form. The authors would also like to thank Sheridon Sauer for her very helpful assistance during the work on the manuscript.
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Losko, S., Heumann, K. (2017). Semantic Data Integration and Knowledge Management to Represent Biological Network Associations. In: Tatarinova, T., Nikolsky, Y. (eds) Biological Networks and Pathway Analysis. Methods in Molecular Biology, vol 1613. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7027-8_16
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DOI: https://doi.org/10.1007/978-1-4939-7027-8_16
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