Large-scale data and text mining

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Lars Juhl Jensen

Wissenschaft

Korbel et al., Nature Biotechnology, 2004

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

Franceschini et al., Nucleic Acids Research, 2013

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
O’Donoghue et al., Journal of Web Semantics, 2010

Gene and protein names
Cue words for entity
recognition
Verbs for relation extraction
[nxexpr The expression of
[nxgene the cytochrome
genes
[nxpg CYC1 and CYC7]]]
is controlled by
[nxpg HAP1]

Jensen et al., Nature Reviews Genetics, 2012

Eriksson et al., submitted, 2013
Drug substance ADE p-value
Chlordiazepoxide Nystagmus 4.0e-8
Simvastatin Personality
changes
8.4e-8
Dipyridamole Visual impairment 4.4e-4
Citalopram Psychosis 8.8e-4
Bendroflumethiazi
de
Apoplexy 8.5e-3

Jensen et al., submitted, 2013
Female Male
In-patientOut-patientEmergencyroom

Acknowledgments
STRING
Christian von
Mering
Damian
Szklarczyk
Michael Kuhn
Manuel Stark
Samuel Chaffron
Chris Creevey
Jean Muller
Tobias Doerks
Philippe Julien
Alexander Roth
Milan Simonovic
Jan Korbel
Berend Snel
Martijn Huynen
Peer Bork
Text mining
Sune Frankild
Jasmin Saric
Evangelos Pafilis
Kalliopi Tsafou
Alberto Santos
Janos Binder
Heiko Horn
Michael Kuhn
Nigel Brown
Reinhardt Schneider
Sean O’ Donoghue
EHR mining
Anders Boeck
Jensen
Peter Bjødstrup
Jensen
Robert Eriksson
Francisco S. Roque
Henriette Schmock
Marlene Dalgaard
Massimo Andreatta
Thomas Hansen
Karen Søeby
Søren Bredkjær
Anders Juul
Tudor Oprea
Pope Moseley
Thomas Werge
Søren Brunak

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Large-scale data and text mining

1. Network biology Large-scale data and text mining Lars Juhl Jensen

2. protein networks

3. medical networks

4. guilt by association

6. protein networks

7. STRING

8. functional associations

9. computational predictions

10. gene fusion

11. Korbel et al., Nature Biotechnology, 2004

12. gene neighborhood

13. Korbel et al., Nature Biotechnology, 2004

14. phylogenetic profiles

15. Korbel et al., Nature Biotechnology, 2004

16. experimental data

17. gene coexpression

18.

19. protein interactions

20. Jensen & Bork, Science, 2008

21. curated knowledge

22. complexes

23. pathways

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

25. many databases

26. different formats

27. different identifiers

28. variable quality

29. not comparable

30. not same species

31. hard work

32. quality scores

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

34. calibrate vs. gold standard

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

36. homology-based transfer

37. Franceschini et al., Nucleic Acids Research, 2013

38. vizualization

39. string-db.org

40. missing most of the data

41. text mining

42. >10 km

43. too much to read

44. computer

45. as smart as a dog

46. teach it specific tricks

47.

48.

49. named entity recognition

50. comprehensive lexicon

51. CDC2

52. cyclin dependent kinase 1

53. expansion rules

54. hCdc2

55. CDC2

56. flexible matching

57. cyclin-dependent kinase 1

58. cyclin dependent kinase 1

59. “black list”

60. SDS

61. augmented browsing

62. Reflect

63. browser add-on

64. real-time text mining

65. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010

66. information extraction

67. co-mentioning

68. within documents

69. within paragraphs

70. within sentences

71. natural language processing

72. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]

73. text corpus

74. ~22 million abstracts

75. millions of full-text articles

76. medical networks

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

78.

79. opt-out

80. opt-in

81. structured data

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

83. unstructured data

84.

85. Danish

86. busy doctors

87. psychiatric patients

88. custom dictionaries

89. drugs

90. adverse drug events

91. complex filters

92. Eriksson et al., submitted, 2013

93. new adverse drug reactions

94. Eriksson et al., submitted, 2013 Drug substance ADE p-value Chlordiazepoxide Nystagmus 4.0e-8 Simvastatin Personality changes 8.4e-8 Dipyridamole Visual impairment 4.4e-4 Citalopram Psychosis 8.8e-4 Bendroflumethiazi de Apoplexy 8.5e-3

95. temporal correlation

96. diagnosis trajectories

97. Jensen et al., in preparation, 2013

98. national discharge registry

99. 6.2 million patients

100. 14 years

101. confounding factors

102. age and gender

103. Jensen et al., submitted, 2013 Female Male In-patientOut-patientEmergencyroom

104. lifestyle

105. reporting biases

106. complex trajectories

107. Jensen et al., submitted, 2013

108. medical implications

109. Acknowledgments STRING Christian von Mering Damian Szklarczyk Michael Kuhn Manuel Stark Samuel Chaffron Chris Creevey Jean Muller Tobias Doerks Philippe Julien Alexander Roth Milan Simonovic Jan Korbel Berend Snel Martijn Huynen Peer Bork Text mining Sune Frankild Jasmin Saric Evangelos Pafilis Kalliopi Tsafou Alberto Santos Janos Binder Heiko Horn Michael Kuhn Nigel Brown Reinhardt Schneider Sean O’ Donoghue EHR mining Anders Boeck Jensen Peter Bjødstrup Jensen Robert Eriksson Francisco S. Roque Henriette Schmock Marlene Dalgaard Massimo Andreatta Thomas Hansen Karen Søeby Søren Bredkjær Anders Juul Tudor Oprea Pope Moseley Thomas Werge Søren Brunak