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Computational Biology Signaling networks and drug repositioning Lars Juhl Jensen
 
 
sequence analysis
Jensen, Gupta et al.,  Journal of Molecular Biology , 2002
 
 
data integration
de Lichtenberg, Jensen et al.,  Science , 2005
 
 
text mining
Pafilis, O’Donoghue, Jensen et al.,  Nature Biotechnology , 2009
human variation
 
signaling networks
phosphoproteomics
 
in vivo  phosphosites
kinases are unknown
sequence motifs
Miller, Jensen et al.,  Science Signaling , 2008
NetPhorest
data organization
Miller, Jensen et al.,  Science Signaling , 2008
automated pipeline
Miller, Jensen et al.,  Science Signaling , 2008
compilation of datasets
training and evaluation
motif atlas
 
179 kinases
89 SH2 domains
8 PTB domains
BRCT domains
WW domains
14-3-3 proteins
sequence specificity
kinase-specific
in vitro
network context
Linding, Jensen, Ostheimer et al.,  Cell , 2007
STRING
Jensen, Kuhn et al.,  Nucleic Acids Research , 2009
630 genomes
2.5 million proteins
genomic context
gene fusion
Korbel et al.,  Nature Biotechnology , 2004
phylogenetic profiles
Korbel et al.,  Nature Biotechnology , 2004
primary experimental data
physical interactions
Jensen & Bork,  Science , 2008
gene coexpression
 
curated knowledge
Letunic & Bork,  Trends in Biochemical Sciences , 2008
literature mining
 
not comparable
confidence scores
von Mering et al.,  Nucleic Acids Research , 2005
cross-species integration
Linding, Jensen, Ostheimer et al.,  Cell , 2007
putting it all together
NetworKIN
Linding, Jensen, Ostheimer et al.,  Cell , 2007
>2x better accuracy
use case
DNA damage response
Linding, Jensen, Ostheimer et al.,  Cell , 2007
experimental validation
ATM phosphorylates Rad50
Linding, Jensen, Ostheimer et al.,  Cell , 2007
drug repositioning
new uses for old drugs
drug–drug network
shared target(s)
chemical similarity
Tanimoto coefficients
Campillos & Kuhn et al.,  Science , 2008
Campillos & Kuhn et al.,  Science , 2008
similar drugs share targets
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
side-effect ontology
backtracking
Campillos & Kuhn et al.,  Science , 2008
side-effect correlations
Campillos & Kuhn et al.,  Science , 2008
GSC weighting
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
confidence scores
drug–drug network
Campillos & Kuhn et al.,  Science , 2008
categorization
Campillos & Kuhn et al.,  Science , 2008
experimental validation
20 drug–drug relations
in vitro  binding assays
K i <10 µM for 11 of 20
cell assays
9 of 9 showed activity
summary
computational biology
network analysis
testable predictions
save much time in the lab
Acknowledgments <ul><li>NetPhorest.info </li></ul><ul><ul><li>Rune Linding </li></ul></ul><ul><ul><li>Martin Lee Miller </...
larsjuhljensen
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Computational Biology - Signaling networks and drug repositioning

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Medical Research Methodology in Translational Medicine, University of Copenhagen, Copenhagen, August 24, 2010.

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Computational Biology - Signaling networks and drug repositioning

  1. 1. Computational Biology Signaling networks and drug repositioning Lars Juhl Jensen
  2. 4. sequence analysis
  3. 5. Jensen, Gupta et al., Journal of Molecular Biology , 2002
  4. 8. data integration
  5. 9. de Lichtenberg, Jensen et al., Science , 2005
  6. 12. text mining
  7. 13. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009
  8. 14. human variation
  9. 16. signaling networks
  10. 17. phosphoproteomics
  11. 19. in vivo phosphosites
  12. 20. kinases are unknown
  13. 21. sequence motifs
  14. 22. Miller, Jensen et al., Science Signaling , 2008
  15. 23. NetPhorest
  16. 24. data organization
  17. 25. Miller, Jensen et al., Science Signaling , 2008
  18. 26. automated pipeline
  19. 27. Miller, Jensen et al., Science Signaling , 2008
  20. 28. compilation of datasets
  21. 29. training and evaluation
  22. 30. motif atlas
  23. 32. 179 kinases
  24. 33. 89 SH2 domains
  25. 34. 8 PTB domains
  26. 35. BRCT domains
  27. 36. WW domains
  28. 37. 14-3-3 proteins
  29. 38. sequence specificity
  30. 39. kinase-specific
  31. 40. in vitro
  32. 41. network context
  33. 42. Linding, Jensen, Ostheimer et al., Cell , 2007
  34. 43. STRING
  35. 44. Jensen, Kuhn et al., Nucleic Acids Research , 2009
  36. 45. 630 genomes
  37. 46. 2.5 million proteins
  38. 47. genomic context
  39. 48. gene fusion
  40. 49. Korbel et al., Nature Biotechnology , 2004
  41. 50. phylogenetic profiles
  42. 51. Korbel et al., Nature Biotechnology , 2004
  43. 52. primary experimental data
  44. 53. physical interactions
  45. 54. Jensen & Bork, Science , 2008
  46. 55. gene coexpression
  47. 57. curated knowledge
  48. 58. Letunic & Bork, Trends in Biochemical Sciences , 2008
  49. 59. literature mining
  50. 61. not comparable
  51. 62. confidence scores
  52. 63. von Mering et al., Nucleic Acids Research , 2005
  53. 64. cross-species integration
  54. 65. Linding, Jensen, Ostheimer et al., Cell , 2007
  55. 66. putting it all together
  56. 67. NetworKIN
  57. 68. Linding, Jensen, Ostheimer et al., Cell , 2007
  58. 69. >2x better accuracy
  59. 70. use case
  60. 71. DNA damage response
  61. 72. Linding, Jensen, Ostheimer et al., Cell , 2007
  62. 73. experimental validation
  63. 74. ATM phosphorylates Rad50
  64. 75. Linding, Jensen, Ostheimer et al., Cell , 2007
  65. 76. drug repositioning
  66. 77. new uses for old drugs
  67. 78. drug–drug network
  68. 79. shared target(s)
  69. 80. chemical similarity
  70. 81. Tanimoto coefficients
  71. 82. Campillos & Kuhn et al., Science , 2008
  72. 83. Campillos & Kuhn et al., Science , 2008
  73. 84. similar drugs share targets
  74. 85. only trivial predictions
  75. 86. phenotypic similarity
  76. 87. chemical perturbations
  77. 88. phenotypic readouts
  78. 89. drug treatment
  79. 90. side effects
  80. 91. no database
  81. 92. package inserts
  82. 93. Campillos & Kuhn et al., Science , 2008
  83. 94. text mining
  84. 95. side-effect ontology
  85. 96. backtracking
  86. 97. Campillos & Kuhn et al., Science , 2008
  87. 98. side-effect correlations
  88. 99. Campillos & Kuhn et al., Science , 2008
  89. 100. GSC weighting
  90. 101. side-effect frequencies
  91. 102. Campillos & Kuhn et al., Science , 2008
  92. 103. raw similarity score
  93. 104. Campillos & Kuhn et al., Science , 2008
  94. 105. p-values
  95. 106. Campillos & Kuhn et al., Science , 2008
  96. 107. side-effect similarity
  97. 108. chemical similarity
  98. 109. Campillos & Kuhn et al., Science , 2008
  99. 110. confidence scores
  100. 111. drug–drug network
  101. 112. Campillos & Kuhn et al., Science , 2008
  102. 113. categorization
  103. 114. Campillos & Kuhn et al., Science , 2008
  104. 115. experimental validation
  105. 116. 20 drug–drug relations
  106. 117. in vitro binding assays
  107. 118. K i <10 µM for 11 of 20
  108. 119. cell assays
  109. 120. 9 of 9 showed activity
  110. 121. summary
  111. 122. computational biology
  112. 123. network analysis
  113. 124. testable predictions
  114. 125. save much time in the lab
  115. 126. Acknowledgments <ul><li>NetPhorest.info </li></ul><ul><ul><li>Rune Linding </li></ul></ul><ul><ul><li>Martin Lee Miller </li></ul></ul><ul><ul><li>Francesca Diella </li></ul></ul><ul><ul><li>Claus Jørgensen </li></ul></ul><ul><ul><li>Michele Tinti </li></ul></ul><ul><ul><li>Lei Li </li></ul></ul><ul><ul><li>Marilyn Hsiung </li></ul></ul><ul><ul><li>Sirlester A. Parker </li></ul></ul><ul><ul><li>Jennifer Bordeaux </li></ul></ul><ul><ul><li>Thomas Sicheritz-Pontén </li></ul></ul><ul><ul><li>Marina Olhovsky </li></ul></ul><ul><ul><li>Adrian Pasculescu </li></ul></ul><ul><ul><li>Jes Alexander </li></ul></ul><ul><ul><li>Stefan Knapp </li></ul></ul><ul><ul><li>Nikolaj Blom </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><ul><li>Shawn Li </li></ul></ul><ul><ul><li>Gianni Cesareni </li></ul></ul><ul><ul><li>Tony Pawson </li></ul></ul><ul><ul><li>Benjamin E. Turk </li></ul></ul><ul><ul><li>Michael B. Yaffe </li></ul></ul><ul><ul><li>Søren Brunak </li></ul></ul><ul><li>STRING-DB.org </li></ul><ul><ul><li>Christian von Mering </li></ul></ul><ul><ul><li>Damian Szklarczyk </li></ul></ul><ul><ul><li>Michael Kuhn </li></ul></ul><ul><ul><li>Manuel Stark </li></ul></ul><ul><ul><li>Samuel Chaffron </li></ul></ul><ul><ul><li>Chris Creevey </li></ul></ul><ul><ul><li>Jean Muller </li></ul></ul><ul><ul><li>Tobias Doerks </li></ul></ul><ul><ul><li>Philippe Julien </li></ul></ul><ul><ul><li>Alexander Roth </li></ul></ul><ul><ul><li>Milan Simonovic </li></ul></ul><ul><ul><li>Jan Korbel </li></ul></ul><ul><ul><li>Berend Snel </li></ul></ul><ul><ul><li>Martijn Huynen </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><li>Side effect </li></ul><ul><ul><li>Monica Campillos </li></ul></ul><ul><ul><li>Michael Kuhn </li></ul></ul><ul><ul><li>Christian von Mering </li></ul></ul><ul><ul><li>Anne-Claude Gavin </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><li>NetworKIN.info </li></ul><ul><ul><li>Rune Linding </li></ul></ul><ul><ul><li>Gerard Ostheimer </li></ul></ul><ul><ul><li>Heiko Horn </li></ul></ul><ul><ul><li>Martin Lee Miller </li></ul></ul><ul><ul><li>Francesca Diella </li></ul></ul><ul><ul><li>Karen Colwill </li></ul></ul><ul><ul><li>Jing Jin </li></ul></ul><ul><ul><li>Pavel Metalnikov </li></ul></ul><ul><ul><li>Vivian Nguyen </li></ul></ul><ul><ul><li>Adrian Pasculescu </li></ul></ul><ul><ul><li>Jin Gyoon Park </li></ul></ul><ul><ul><li>Leona D. Samson </li></ul></ul><ul><ul><li>Rob Russell </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><ul><li>Michael Yaffe </li></ul></ul><ul><ul><li>Tony Pawson </li></ul></ul>
  116. 127. larsjuhljensen

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