Network integration of data and text

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Lars Juhl Jensen

Network integration of data and text Lars Juhl Jensen

Part 1 text mining

>10 km

exponential growth

law of diminishing returns

some things are constant

~45 seconds per paper

computer

as smart as a dog

teach it specific tricks

named entity recognition

Reflect

augmented browsing

browser add-on

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009

collaborations

web services

Utopia Documents

information extraction

co-mentioning

<10 hours

no access

Part 2 protein networks

STRING

Szklarczyk, Franceschini et al., Nucleic Acids Research , 2011

630 genomes

many databases

genomic context

gene fusion

Korbel et al., Nature Biotechnology , 2004

conserved neighborhood

operons

Korbel et al., Nature Biotechnology , 2004

bidirectional promoters

Korbel et al., Nature Biotechnology , 2004

phylogenetic profiles

Korbel et al., Nature Biotechnology , 2004

experimental data

physical interactions

Jensen & Bork, Science , 2008

gene coexpression

curated knowledge

pathways

Letunic & Bork, Trends in Biochemical Sciences , 2008

text mining

many data types

many databases

different formats

different identifiers

variable quality

quality scores

calibrate vs. gold standard

von Mering et al., Nucleic Acids Research , 2005

orthology transfer

Frishman et al., Modern Genome Annotation , 2009

Part 3 small molecule networks

STITCH

Kuhn et al., Nucleic Acids Research , 2010

in vitro binding assays

text mining

chemical similarity

Campillos & Kuhn et al., Science , 2008

similar drugs share targets

Campillos & Kuhn et al., Science , 2008

only trivial predictions

phenotypic similarity

chemical perturbations

phenotypic readouts

drug treatment

side effects

no database

package inserts

Campillos & Kuhn et al., Science , 2008

text mining

manual validation

side-effect correlations

Campillos & Kuhn et al., Science , 2008

side-effect frequencies

Campillos & Kuhn et al., Science , 2008

raw similarity score

Campillos & Kuhn et al., Science , 2008

p-values

Campillos & Kuhn et al., Science , 2008

side-effect similarity

chemical similarity

Campillos & Kuhn et al., Science , 2008

drug–drug network

Campillos & Kuhn et al., Science , 2008

categorization

Campillos & Kuhn et al., Science , 2008

20 drug–drug pairs

in vitro binding assays

K i <10 µM for 11 of 20

cell assays

9 of 9 showed activity

Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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1. Network integration of data and text Lars Juhl Jensen

2. Part 1 text mining

4. exponential growth

5.

6.

7. law of diminishing returns

8. some things are constant

9.

10. ~45 seconds per paper

12. as smart as a dog

13. teach it specific tricks

14.

15.

16. named entity recognition

18. augmented browsing

19. browser add-on

20. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009

21. collaborations

22.

23. web services

24.

25. Utopia Documents

26. information extraction

27. co-mentioning

28.

31. Part 2 protein networks

33. Szklarczyk, Franceschini et al., Nucleic Acids Research , 2011

34. 630 genomes

35. many databases

36. genomic context

37. gene fusion

38. Korbel et al., Nature Biotechnology , 2004

39. conserved neighborhood

41. Korbel et al., Nature Biotechnology , 2004

42. bidirectional promoters

43. Korbel et al., Nature Biotechnology , 2004

44. phylogenetic profiles

45. Korbel et al., Nature Biotechnology , 2004

46. experimental data

47. physical interactions

48. Jensen & Bork, Science , 2008

49. gene coexpression

50.

51. curated knowledge

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

54. text mining

55.

56. many data types

57. many databases

58. different formats

59. different identifiers

60. variable quality

61. quality scores

62. calibrate vs. gold standard

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

64. orthology transfer

65. Frishman et al., Modern Genome Annotation , 2009

66. Part 3 small molecule networks

68. Kuhn et al., Nucleic Acids Research , 2010

69. in vitro binding assays

70. text mining

71. chemical similarity

72. Campillos & Kuhn et al., Science , 2008

73. similar drugs share targets

74. Campillos & Kuhn et al., Science , 2008

75. only trivial predictions

76. phenotypic similarity

77. chemical perturbations

78. phenotypic readouts

79. drug treatment

80. side effects

81. no database

82. package inserts

83. Campillos & Kuhn et al., Science , 2008

84. text mining

85. manual validation

86. side-effect correlations

87. Campillos & Kuhn et al., Science , 2008

88. side-effect frequencies

89. Campillos & Kuhn et al., Science , 2008

90. raw similarity score

91. Campillos & Kuhn et al., Science , 2008

93. Campillos & Kuhn et al., Science , 2008

94. side-effect similarity

95. chemical similarity

96. Campillos & Kuhn et al., Science , 2008

97. drug–drug network

98. Campillos & Kuhn et al., Science , 2008

99. categorization

100. Campillos & Kuhn et al., Science , 2008

101. 20 drug–drug pairs

102. in vitro binding assays

103. K i <10 µM for 11 of 20

104. cell assays

105. 9 of 9 showed activity

106.

107. larsjuhljensen

108.