This document discusses natural language processing (NLP) techniques for extracting information from biomedical literature and integrating it with network and interaction data. It describes how NLP is used to identify entities like genes and proteins, extract relationships between entities, and integrate this text-mined information with existing interaction networks from databases like STRING to expand knowledge of protein interactions, complexes, pathways and associations with diseases. The document provides examples of using NLP analysis on sentences and the STRING and Tissues databases to explore tissue specificity and disease relationships for insulin and the insulin receptor.

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

2. interaction networks

3. association networks

4. guilt by association

6. protein networks

7. STRING

8. 9.6 million proteins

9. common foundation

10. Exercise 1
Go to http://string-db.org/
Query for human insulin receptor
(INSR) using the search by name
functionality
Make sure you are in evidence view
(check the buttons below the
network)
Why are there multiple lines

11. curated knowledge

12. (what we know)

13. protein complexes

14. 3D structures

16. pathways

17. metabolic pathways

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

19. signaling pathways

20. very incomplete

21. experimental data

22. (what we measured)

23. physical interactions

24. Jensen & Bork, Science, 2008

25. genetic interactions

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

27. gene coexpression

28. microarrays

29. RNAseq

30. Exercise 2
(Continue from where exercise 1
ended)
Which types of evidence support the
interaction between INSR and IRS1?
Click on the interaction to view the
popup, which has buttons linking to
full details
Which types of experimental assays

31. predictions

32. (what we infer)

33. genomic context

34. evolution

35. gene fusion

36. Korbel et al., Nature Biotechnology, 2004

37. gene neighborhood

38. Korbel et al., Nature Biotechnology, 2004

39. phylogenetic profiles

40. Korbel et al., Nature Biotechnology, 2004

41. a real example

45. Cell
Cellulosomes
Cellulose

46. complications

47. many databases

48. different formats

49. different identifiers

50. variable quality

51. not comparable

52. not same species

53. hard work

54. parsers

55. mapping files

56. quality scores

57. affinity purification

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

59. phylogenetic profiles

61. score calibration

62. gold standard

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

64. implicit weighting by quality

65. common scale

66. homology-based transfer

67. orthologous groups

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

69. missing most of the data

70. Exercise 3
(Continue from where exercise 2
ended)
Change the network to the
confidence view
Change the confidence cutoff to 0.9;
any changes in proteins or
interactions shown?
Turn off all but experiments; what

71. text mining

72. >10 km

73. too much to read

74. exponential growth

75. ~40 seconds per paper

76. computer

77. as smart as a dog

78. teach it specific tricks

81. named entity recognition

82. comprehensive lexicon

83. cyclin dependent kinase 1

84. CDC2

85. orthographic variation

86. expansion rules

87. prefixes and suffixes

88. CDC2

89. hCdc2

90. flexible matching

91. spaces and hyphens

92. cyclin dependent kinase 1

93. cyclin-dependent kinase 1

94. “black list”

95. SDS

96. information extraction

97. co-mentioning

98. counting

99. within documents

100. within paragraphs

101. within sentences

102. scoring scheme

105. score calibration

106. NLP
Natural Language Processing

107. part-of-speech tagging

108. what you learned in school
pronoun pronoun verb preposition noun

109. semantic tagging

110. grammatical analysis

111. 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]
Saric et al., Proceedings of ACL, 2004

112. type and direction

113. complex sentences

114. anaphoric references

115. it

116. related resources

117. protein networks

118. Szklarczyk et al., Nucleic Acids Research, 2015string-db.org

119. STITCH

120. chemical networks

121. Kuhn et al., Nucleic Acids Research, 2014stitch-db.org

122. COMPARTMENTS

123. subcellular localization

124. Binder et al., Database, 2014compartments.jensenlab.org

125. TISSUES

126. tissue expression

127. tissues.jensenlab.org Santos et al., PeerJ, 2015

128. disease associations

129. diseases.jensenlab.org Frankild et al., Methods, 2015

130. Exercise 4
Open http://tissues.jensenlab.org
Look up tissue associations for insulin
(INS)
Open http://diseases.jensenlab.org
Search for insulin receptor (INSR)
What is the strongest associated
disease?

131. network analysis

132. Cytoscape

133. three types of queries

134. protein query

139. disease query

143. PubMed query

146. questions?