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Biomedical Data/Content Acquisition, Curation

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Encyclopedia of Database Systems
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Synonyms

Biomedical data annotation

Definition

The largest source of biomedical knowledge is the published literature, where results of experimental studies are reported in natural language. Published literature is hard to query, integrate computationally or to reason over. The task of reading published papers (or other forms of experimental results such as pharmacogenomics datasets) and distilling them down into structured knowledge that can be stored in databases as well as knowledgebases is called curation. The statements comprising the structured knowledge are called annotations. The level of structure in annotation statements can vary from loose declarations of “associations” between concepts (such as associating a paper with the concept “colon cancer”) to statements that declare a precisely defined relationship between concepts with explicit semantics. There is an inherent tradeoff between the level of detail of the structured annotations and the time and effort required to...

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Recommended Reading

  1. Ashburner M., et al. Gene ontology: tool for the unification of biology. Nat. Genet., 25(1):25–29, 2000.

    Article  Google Scholar 

  2. Baral C., et al. A knowledge based approach for representing and reasoning about signaling networks. Bioinformatics, 20(1):15–22, 2004.

    Article  Google Scholar 

  3. Baumgartner Jr, W.A. et al. Manual curation is not sufficient for annotation of genomic databases. Bioinformatics, 23(13):41–48, 2007.

    Google Scholar 

  4. BioPax-Consortium. BioPAX: Biological Pathways Exchange. Available from: http://www.biopax.org/2006.

  5. Bodenreider O. and Stevens R. Bio-ontologies: current trends and future directions. Brief. Bioinform., 7(3):256–274, 2006.

    Article  Google Scholar 

  6. Camon E.B., 2005.et al. An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. BMC Bioinform., 6(Suppl 1):S17,

    Google Scholar 

  7. Cherry J.M., et al. SGD: saccharomyceas genome database. Nucleic Acids Res., 26(1):73–79, 1998.

    Article  Google Scholar 

  8. Ciccaresse P., Wu E., and Clark T. An overview of the SWAN 1.0 ontology of scientific discourse. In Proc. 16th Int. World Wide Web Conference, 2007.

    Google Scholar 

  9. Clark T. and Kinoshita J. Alzforum and SWAN: the present and future of scientific web communities. Brief. Bioinform., 8(3):163–171, 2007.

    Article  Google Scholar 

  10. Gao Y., et al. SWAN: a distributed knowledge infrastructure for Alzheimer disease research. J. Web Semantics, 4(3):222–228, 2006.

    Google Scholar 

  11. Gibson M. Phenote. Berkeley Bioinformatics and Ontology Project (BBOP), National Center for Biomedical Ontology, Lawrence Berkeley National Laboratory, 2007.

    Google Scholar 

  12. Hunter L. and Cohen K.B. Biomedical language processing: what’s beyond PubMed? Mol. Cell., 21(5):589–594, 2006.

    Article  Google Scholar 

  13. Joshi-Tope G., et al. Reactome: a knowledge base of biological pathways. Nucleic Acids Res., 33(Database Issue): D428–432, 2005.

    Article  Google Scholar 

  14. Karp P.D. An ontology for biological function based on molecular interactions. Bioinformatics, 16(3):269–285, 2000.

    Article  Google Scholar 

  15. Karp P.D. Pathway databases: a case study in computational symbolic theories. Science, 293(5537):2040–2044, 2001.

    Article  Google Scholar 

  16. Katz A.E., et al. Molecular staging of genitourinary malignancies. Urology, 47(6):948–958, 1996.

    Google Scholar 

  17. Leslie M. Netwatch. Science, 312:1721, 2006.

    Google Scholar 

  18. Massar J.P., et al. BioLingua: a programmable knowledge environment for biologists. Bioinformatics, 21(2):199–207, 2004.

    Google Scholar 

  19. Racunas S.A., et al. HyBrow: a prototype system for computer-aided hypothesis evaluation. Bioinformatics, 20(Suppl 1):257–264, 2004.

    Google Scholar 

  20. Reactome Curator Guide. http://wiki.reactome.org/index.php/Reactome_Curator_Guide

  21. Rise of the Bio-Librarian – the field of biocuration expands as the data grow. http://www.the-scientist.com/article/display/23316/.

  22. Ruttenberg A., 2007.et al. Advancing translational research with the Semantic Web. BMC Bioinform., 8(Suppl 3):S2,

    Google Scholar 

  23. Rzhetsky A., et al. GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J. Biomed. Inform., 37(1):43–53, 2004.

    Article  Google Scholar 

  24. Second International Biocuration Meeting, San Jose, CA, October 25–28, 2007. http://biocurator.org/Mtg2007/index.html.

  25. Shrager J., et al. 2007.Deductive biocomputing. PLoS ONE, 2(4):e339,

    Article  Google Scholar 

  26. Sim I., Olasov B., and Carini S. The Trial Bank system: capturing randomized trials for evidence-based medicine. AMIA Annu. Symp. Proc., 2003:1076, 2003.

    Google Scholar 

  27. Smith B., et al. 2005.Relations in biomedical ontologies. Genome Biol., 6(5):R46,

    Article  Google Scholar 

  28. Spasic I., Ananiadou S., McNaught J., and Kumar A. Text mining and ontologies in biomedicine: making sense of raw text. Brief. Bioinform. 6(3):239–251, 2005.

    Article  Google Scholar 

  29. Tari L., et al. BioQA. http://cbioc.eas.asu.edu/bioQA/v2/index.html, 2007.

  30. The National Center for Biomedical Ontology. Available at: www.biontology.org, 2006.

  31. Thorn C.F., Klein T.E., and Altman R.B. PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base. Meth. Mol. Biol., 311:179–91, 2005.

    Google Scholar 

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Shah, N. (2009). Biomedical Data/Content Acquisition, Curation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_37

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