Network Biology: Large-scale integration of data and text

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Lars Juhl Jensen leads a group that conducts large-scale integration of biological and medical data using proteomics, text mining, and medical data mining. The group develops protein interaction networks, disease networks, and association networks. They collaborate internationally on projects involving over 9.6 million proteins and 2000 genomes. The group works to integrate data from many sources in different formats to build comprehensive networks and knowledgebases, and also mines biomedical text to link genes and proteins with diseases.

Lars Juhl Jensen
Network Biology
Large-scale integration of data and
text

Jensen et al., Nature Reviews Genetics, 2012

Jensen et al., Nature Communications, 2014

Korbel et al., Nature Biotechnology, 2004

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

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Eriksson et al., Drug Safety, 2014
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Network Biology: Large-scale integration of data and text

1. Lars Juhl Jensen Network Biology Large-scale integration of data and text

2. me

3. group leader

5. cofounder

7. disease systems biology

9. proteomics data

10. mass spectrometry

11.

12. protein networks

13. Hendriks et al., NSMB, 2017

14. medical data

15. Jensen et al., Nature Reviews Genetics, 2012

16. disease networks

17. Jensen et al., Nature Communications, 2014

18. association networks

19. guilt by association

20.

21. protein networks

22. string-db.org

23. international collaboration

24. 9.6 million proteins

25. 2000+ genomes

26. genomic context

27. gene fusion

28. Korbel et al., Nature Biotechnology, 2004

29. experimental data

30. coexpression

31. physical interactions

32. Jensen & Bork, Science, 2008

33. curated knowledge

34. pathways

35. Letunic & Bork, Trends in Biochemical Sciences, 2008

36. many databases

37. different formats

38. different identifiers

39. variable quality

40. not comparable

41. hard work

42. quality scores

43. affinity purification

44. von Mering et al., Nucleic Acids Research, 2005

45. score calibration

46. gold standard

47. von Mering et al., Nucleic Acids Research, 2005

48. implicit weighting by quality

49. common scale

50. missing most of the data

51. text mining

52. >10 km

53. too much to read

54. computer

55. as smart as a dog

56. teach it specific tricks

57.

58.

59. named entity recognition

60. dictionary

61. cyclin dependent kinase 1

62. CDC2

63. orthographic variation

64. spaces and hyphens

65. cyclin dependent kinase 1

66. cyclin-dependent kinase 1

67. prefixes and suffixes

68. CDC2

69. hCdc2

70. “black list”

71. SDS

72. co-mentioning

73. counting

74. scoring scheme

75. score calibration

76. medical data

77. Jensen et al., Nature Reviews Genetics, 2012

78. structured data

79. Jensen et al., Nature Reviews Genetics, 2012

80. unstructured data

81.

82. Danish

83. busy doctors

84. abbreviations

85. misspellings

86. adverse drug reactions

87. drugs

88. Clozapine

89. Clozapine clozapi n clossapi n klozapin e chlosapi n chlosapi ne chlozapi n chlozapi ne klossapi n closapin e klozapi nklosapi n

90. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuation Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction Adverse event ADR of additional drug Identification start

91. country-wide registries

92. 119 million diagnoses

93. 6.2 million patients

94. comorbidity

95. confounding factors

96. “known knowns”

97. gender

98. age

99. type of hospital encounter

100. Jensen et al., Nature Communications, 2014

101. “known unknowns”

102. smoking