Visualization of large-scaleprotein and disease networks

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Lars Juhl Jensen

Visualization of large-scaleprotein and disease networks

Visualization of large-scale
protein and disease networks
Lars Juhl Jensen

the mitotic cell cycle

grow and divide

one cell

two cells

four phases

G1 phase

growth

S phase

DNA replication

G2 phase

growth

M phase

cell division

cell culture

synchronization

microarrays

time courses

Gauthier et al., Nucleic Acids Research, 2007

cycling genes

single-number summary

time of peak expression

circular color scale

system-level visualization

gene expression

protein interactions

temporal network

de Lichtenberg, Jensen et al., Science, 2005

discovery

just-in-time assembly

de Lichtenberg, Jensen et al., Cell Cycle, 2007

protein networks

STRING

web resource

functional associations

heterogeneous evidence

computational predictions

genomic context

Korbel et al., Nature Biotechnology, 2004

experimental data

physical interactions

Jensen & Bork, Science, 2008

curated knowledge

pathways

Letunic & Bork, Trends in Biochemical Sciences, 2008

text mining

biomedical literature

>10 km

common identifiers

quality scores

cross-species transfer

complex information

interactive visualization

string-db.org

inspect evidence

explorative analysis

disease networks

central health registries

CPR number

established in 1968

national discharge registry

14 years

6.2 million patients

45 million admissions

68 million records

119 million diagnosis

distributions

gender

age

type of hospital encounter

Jensen et al., Nature Communications, 2014

temporal correlations

too many to visualize

trajectories

Jensen et al., Nature Communications, 2014

trajectory networks

Jensen et al., Nature Communications, 2014

discovery

key diagnoses

Jensen et al., Nature Communications, 2014

direct medical implications

summary

visualization is important

publishing results

exploring data

making discoveries

Acknowledgments
STRING
Michael Kuhn
Damian Szklarczyk
Andrea Franceschini
Milan Simonovic
Alexander Roth
Sune Pletscher-
Frankild
Jianyi Lin
Pablo Minguez
Christian von Mering
Peer Bork
Cell cycle
Ulrik de Lichtenberg
Thomas Skøt Jensen
Søren Brunak
Peer Bork
Disease networks
Anders Bøck Jensen
Tudor Oprea
Pope Moseley
Søren Brunak

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Visualization of large-scaleprotein and disease networks

1. Visualization of large-scale protein and disease networks Lars Juhl Jensen

2. the mitotic cell cycle

3. grow and divide

10. DNA replication

14. cell division

15.

16. cell culture

17.

18. synchronization

19. microarrays

20.

21. time courses

22. Gauthier et al., Nucleic Acids Research, 2007

23. cycling genes

24. single-number summary

25. time of peak expression

26. circular color scale

27.

28. system-level visualization

29. gene expression

30. protein interactions

31. temporal network

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

34. just-in-time assembly

35. de Lichtenberg, Jensen et al., Cell Cycle, 2007

36. protein networks

38. web resource

39. functional associations

40. heterogeneous evidence

41. computational predictions

42. genomic context

43. Korbel et al., Nature Biotechnology, 2004

44. experimental data

45. physical interactions

46. Jensen & Bork, Science, 2008

47. curated knowledge

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

50. text mining

51. biomedical literature

53. common identifiers

54. quality scores

55. cross-species transfer

56. complex information

57. interactive visualization

58. string-db.org

59. inspect evidence

60. explorative analysis

61. disease networks

62. central health registries

64. established in 1968

65. national discharge registry

67. 6.2 million patients

68. 45 million admissions

69. 68 million records

70. 119 million diagnosis

71. distributions

74. type of hospital encounter

75. Jensen et al., Nature Communications, 2014

76. temporal correlations

77. too many to visualize

78. trajectories

79. Jensen et al., Nature Communications, 2014

80. trajectory networks

81. Jensen et al., Nature Communications, 2014

83. key diagnoses

84. Jensen et al., Nature Communications, 2014

85. direct medical implications

87. visualization is important

88. publishing results

89. exploring data

90. making discoveries

91. Acknowledgments STRING Michael Kuhn Damian Szklarczyk Andrea Franceschini Milan Simonovic Alexander Roth Sune Pletscher- Frankild Jianyi Lin Pablo Minguez Christian von Mering Peer Bork Cell cycle Ulrik de Lichtenberg Thomas Skøt Jensen Søren Brunak Peer Bork Disease networks Anders Bøck Jensen Tudor Oprea Pope Moseley Søren Brunak