Text-mining practical
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The document provides an overview of using text-mining techniques like named entity recognition and information extraction to identify disease genes from biomedical abstracts. It describes retrieving over 170,000 abstracts related to prostate cancer and schizophrenia and using tools like grep, cut, sort, and uniq to analyze the text and identify human genes mentioned in the abstracts as well as their co-occurrence between diseases. The goal is to leverage these methods to help prioritize candidate genes for further study.
Text-mining practical
- 1. Text-mining practical Lars Juhl Jensen
- 2. unix primer
- 5. cat
- 6. less
- 7. head -10
- 8. tail -10
- 10. cut -f 2
- 11. sort
- 12. sort -nr
- 13. uniq -c
- 15. write to file
- 17. using pipes
- 19. putting it all together
- 20. cut -f 4 infile | sort | uniq -c | sort -nr | head -100 > outfile
- 21. the task
- 24. human genes
- 26. what I have done
- 28. two diseases
- 29. prostate cancer
- 30. schizophrenia
- 31. two sets of documents
- 32. 82,373 abstracts
- 33. 89,904 abstracts
- 34. one file with each set
- 35. one line per abstract
- 36. dictionary
- 38. human genes
- 39. 21,929 entities
- 40. synonyms
- 45. 2,920,042 names
- 51. what you will do
- 56. co-mentioning
- 57. within abstracts
- 58. ank genes for each disease
- 59. find shared gene
- 61. wrap up
- 62. Protein kinase B
- 63. PKB
- 64. Akt
- 65. AKT1
- 66. same protein
- 67. synonyms matter
- 68. “black list” is crucial
- 69. text mining is useful
- 70. not black magic
- 71. Thanks for your attention