Medical network analysis: Linking diseases and genes through data and text mining
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The document summarizes the work of Lars Juhl Jensen and others on medical network analysis and linking diseases and genes through data and text mining of electronic health records. It discusses how they have used Danish national health registries containing data on over 6 million patients and 119 million diagnoses over 14 years to study disease trajectories and comorbidities. It also describes how they have developed methods to integrate data from various sources to generate networks linking diseases and genes.
Medical network analysis: Linking diseases and genes through data and text mining
- 1. Lars Juhl Jensen Medical network analysis Linking diseases and genes through data and text mining
- 5. linking genes and diseases
- 7. Jensen et al., Nature Reviews Genetics, 2012
- 10. Jensen et al., Nature Reviews Genetics, 2012
- 13. CPR number
- 14. Jensen et al., Nature Reviews Genetics, 2012
- 16. 14 years
- 19. Jensen et al., Nature Reviews Genetics, 2012
- 20. reimbursement
- 22. comorbidity
- 24. Jensen et al., Nature Reviews Genetics, 2012
- 26. “known knowns”
- 27. sex
- 28. age
- 29. type of hospital encounter
- 30. Jensen et al., Nature Communications, 2014
- 31. “known unknowns”
- 32. smoking
- 33. diet
- 35. reporting biases
- 36. matched controls
- 39. Jensen et al., Nature Communications, 2014
- 40. clustering
- 42. Jensen et al., Nature Communications, 2014
- 45. Beck et al., Scientific Reports, 2016
- 47. string-db.org
- 49. DISEASES
- 54. pathways
- 58. Jensen & Bork, Science, 2008
- 59. coexpression
- 60. GWAS
- 61. text mining
- 62. >10 km
- 66. many databases
- 69. variable quality
- 70. not comparable
- 71. hard work
- 72. (Ph.D. students)
- 73. quality scores
- 75. von Mering et al., Nucleic Acids Research, 2005
- 78. von Mering et al., Nucleic Acids Research, 2005
- 79. implicit weighting by quality
- 80. common scale
- 81. visualization
- 82. Cytoscape
- 85. Thank you