Data and Text Mining

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Lars Juhl Jensen

Technology Education

Data and Text Mining

Lars Juhl Jensen

sequence analysis

protein networks

de Lichtenberg, Jensen et al., Science, 2005

adverse drug reactions

Campillos, Kuhn et al., Science, 2008

group leader

cofounder

data mining

proteomics

text mining

biomedical literature

electronic health records

protein networks

guilt by association

STRING

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

computational predictions

gene fusion

Korbel et al., Nature Biotechnology, 2004

gene neighborhood

operons

Korbel et al., Nature Biotechnology, 2004

bidirectional promoters

Korbel et al., Nature Biotechnology, 2004

phylogenetic profiles

Korbel et al., Nature Biotechnology, 2004

a real example

Cell

Cellulosomes

Cellulose

experimental data

gene coexpression

protein interactions

Jensen & Bork, Science, 2008

genetic interactions

Beyer et al., Nature Reviews Genetics, 2007

curated knowledge

complexes

pathways

Letunic & Bork, Trends in Biochemical Sciences, 2008

many databases

different formats

different identifiers

variable quality

not comparable

not same species

hard work

quality scores

von Mering et al., Nucleic Acids Research, 2005

calibrate vs. gold standard

von Mering et al., Nucleic Acids Research, 2005

homology-based transfer

Franceschini et al., Nucleic Acids Research, 2013

missing most of the data

text mining

>10 km

too much to read

computer

as smart as a dog

teach it specific tricks

named entity recognition

comprehensive lexicon

CDC2

cyclin dependent kinase 1

expansion rules

hCdc2

CDC2

flexible matching

cyclin-dependent kinase 1

cyclin dependent kinase 1

“black list”

SDS

augmented browsing

Reflect

browser add-on

real-time text mining

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

information extraction

co-mentioning

within documents

within paragraphs

within sentences

text corpus

~22 million abstracts

no access

millions of full-text articles

localization and disease

general approach

COMPARTMENTS

TISSUES

DISEASES

curated knowledge

experimental data

text mining

computational predictions

common identifiers

quality scores

visualization

compartments.jensenlab.org

tissues.jensenlab.org

dissemination

web interfaces

web services

diseases.jensenlab.org

bulk download

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
Evangelos Pafilis
Kalliopi Tsafou
Alberto Santos
Janos Binder
Heiko Horn
Michael Kuhn
Nigel Brown
Reinhardt Schneider
Sean O’ Donoghue

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Data and Text Mining

1. Data and Text Mining Lars Juhl Jensen

2.

3.

4. sequence analysis

5.

6.

7. protein networks

8. de Lichtenberg, Jensen et al., Science, 2005

9. adverse drug reactions

10. Campillos, Kuhn et al., Science, 2008

11.

12. group leader

13.

15.

16. data mining

18. text mining

19. biomedical literature

20. electronic health records

21. protein networks

22. guilt by association

23.

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

26. computational predictions

27. gene fusion

28. Korbel et al., Nature Biotechnology, 2004

29. gene neighborhood

31. Korbel et al., Nature Biotechnology, 2004

32. bidirectional promoters

33. Korbel et al., Nature Biotechnology, 2004

34. phylogenetic profiles

35. Korbel et al., Nature Biotechnology, 2004

36. a real example

37.

38.

39.

40. Cell Cellulosomes Cellulose

41. experimental data

42. gene coexpression

43.

44. protein interactions

45. Jensen & Bork, Science, 2008

46. genetic interactions

47. Beyer et al., Nature Reviews Genetics, 2007

48. curated knowledge

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

52. many databases

53. different formats

54. different identifiers

55. variable quality

56. not comparable

57. not same species

59. quality scores

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

61. calibrate vs. gold standard

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

63. homology-based transfer

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

65. missing most of the data

66. text mining

68. too much to read

70. as smart as a dog

71. teach it specific tricks

72.

73.

74. named entity recognition

75. comprehensive lexicon

77. cyclin dependent kinase 1

78. expansion rules

81. flexible matching

82. cyclin-dependent kinase 1

83. cyclin dependent kinase 1

84. “black list”

86. augmented browsing

88. browser add-on

89. real-time text mining

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

91. information extraction

92. co-mentioning

93. within documents

94. within paragraphs

95. within sentences

96. text corpus

97. ~22 million abstracts

99. millions of full-text articles

100.

101. localization and disease

102. general approach

103. COMPARTMENTS

106. curated knowledge

107. experimental data

108. text mining

109. computational predictions

110. common identifiers

111. quality scores

112. visualization

113. compartments.jensenlab.org

114. tissues.jensenlab.org

115. dissemination

116. web interfaces

117.

118. web services

119. diseases.jensenlab.org

120. bulk download

121. 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 Evangelos Pafilis Kalliopi Tsafou Alberto Santos Janos Binder Heiko Horn Michael Kuhn Nigel Brown Reinhardt Schneider Sean O’ Donoghue