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Biomedical text mining

Biomedical text mining. Lars Juhl Jensen. exponential growth. ~45 seconds per paper. information retrieval. named entity recognition. augmented browsing. text corpora. information extraction. information retrieval. find the relevant papers. ad hoc retrieval. user-specified query.

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Biomedical text mining

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  1. Biomedical text mining Lars Juhl Jensen

  2. exponential growth

  3. ~45 seconds per paper

  4. information retrieval

  5. named entity recognition

  6. augmented browsing

  7. text corpora

  8. information extraction

  9. information retrieval

  10. find the relevant papers

  11. ad hoc retrieval

  12. user-specified query

  13. “yeast AND cell cycle”

  14. PubMed

  15. indexing

  16. fast lookup

  17. stemming

  18. word endings

  19. dynamic query expansion

  20. MeSH terms

  21. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5-dependent Swe1 hyperphosphorylation and degradation

  22. no tool will find that

  23. named entity recognition

  24. computer

  25. as smart as a dog

  26. teach it specific tricks

  27. identify the concepts

  28. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5-dependent Swe1 hyperphosphorylation and degradation

  29. comprehensive lexicon

  30. proteins

  31. chemicals

  32. compartments

  33. tissues

  34. diseases

  35. organisms

  36. CDC2

  37. cyclin dependent kinase 1

  38. orthographic variation

  39. upper- and lower-case

  40. CDC2

  41. Cdc2

  42. spaces and hyphens

  43. cyclin dependent kinase 1

  44. cyclin-dependent kinase 1

  45. prefixes and postfixes

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