1. STRING Protein networks from data and text mining
Lars Juhl Jensen

2. 9.6 million proteins

3. functional associations

4. guilt by association

6. genomic context

7. gene fusion

8. Korbel et al., Nature Biotechnology, 2004

9. phylogenetic profiles

10. Korbel et al., Nature Biotechnology, 2004

11. experimental data

12. gene coexpression

14. physical interactions

15. Jensen & Bork, Science, 2008

16. curated knowledge

17. protein complexes

19. pathways

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

21. many databases

22. different formats

23. different identifiers

24. variable quality

25. not comparable

26. hard work

27. parsers

28. mapping files

29. quality scores

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

31. score calibration

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

33. implicit weighting by quality

34. common scale

35. missing most of the data

36. >10 km

37. too much to read

38. computer

39. as smart as a dog

40. teach it specific tricks

43. named entity recognition

44. comprehensive lexicon

45. cyclin dependent kinase 1

46. CDC2

47. orthographic variation

48. spaces and hyphens

49. cyclin dependent kinase 1

50. cyclin-dependent kinase 1

51. prefixes and suffixes

52. CDC2

53. hCdc2

54. “black list”

55. SDS

56. co-mentioning

57. counting

58. within documents

59. within paragraphs

60. within sentences

61. quality scores

62. score calibration

63. integration

64. visualization

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

66. web resource

67. download files

68. REST API

69. Bioconductor package

70. Cytoscape App

71. protein query

76. disease query

80. Acknowledgments Damian Szklarczyk John "Scooter" Morris Helen Cook
Michael Kuhn
Stefan Wyder
Milan Simonovic
Alberto Santos
Nadezhda Doncheva
Alexander Roth
Peer Bork
Christian von Mering