Network medicine - Integrating drugs, targets, diseases and side-effects

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LEO Pharma, Ballerup, Denmark, November 5, 2008

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Network medicine - Integrating drugs, targets, diseases and side-effects

  1. 1. Lars Juhl Jensen Network medicine Integrating drugs, targets, diseases and side-effects
  2. 2. Lars Juhl Jensen Network medicine Integrating drugs, targets, diseases and side-effects
  3. 3. Lars Juhl Jensen Network medicine Integrating drugs, targets, diseases and side-effects
  4. 6. what went wrong?
  5. 7. phase 0
  6. 8. before you do anything
  7. 9. a good problem
  8. 10. a good idea
  9. 11. dynamics of complexes
  10. 12. microarray data
  11. 13. protein interactions
  12. 14. de Lichtenberg, Jensen, et al., Science , 2005
  13. 15. phosphorylation networks
  14. 16. mass spectrometry data
  15. 17. sequence motifs
  16. 18. functional associations
  17. 19. Linding, Jensen, Ostheimer et al., Cell , 2007
  18. 20. Linding, Jensen, Ostheimer et al., Cell , 2007
  19. 21. the problem
  20. 22. new uses for old drugs
  21. 23. drug–drug network
  22. 24. shared target(s)
  23. 25. chemical similarity
  24. 26. Tanimoto coefficients
  25. 29. similar drugs share targets
  26. 30. only trivial predictions
  27. 31. the idea
  28. 32. chemical perturbations
  29. 33. phenotypic readouts
  30. 34. drug treatment
  31. 35. side effects
  32. 36. the implementation
  33. 37. information on side effects
  34. 38. package inserts
  35. 40. text mining
  36. 41. side-effect ontology
  37. 42. backtracking
  38. 44. side-effect correlations
  39. 46. GSC weighting
  40. 47. side-effect frequencies
  41. 49. raw similarity score
  42. 51. p-values
  43. 53. side-effect similarity
  44. 54. chemical similarity
  45. 56. reference set
  46. 57. drug–target pairs
  47. 59. drug–drug pairs
  48. 60. score bins
  49. 61. benchmark
  50. 63. fit calibration function
  51. 65. probabilistic scores
  52. 66. the results
  53. 67. drug–drug network
  54. 68. ATC codes
  55. 70. categorization
  56. 74. map onto score space
  57. 76. the experiments
  58. 77. 20 drug–drug relations
  59. 78. in vitro binding assays
  60. 82. K i <10 µM for 11 of 20
  61. 83. cell assays
  62. 84. 9 of 9 showed activity
  63. 86. the big picture
  64. 87. STITCH
  65. 89. protein–chemical network
  66. 91. primary experimental data
  67. 92. activity screens
  68. 93. Fedorov et al., PNAS , 2007
  69. 94. protein interactions
  70. 95. Jensen & Bork, Science , 2008
  71. 96. gene coexpression
  72. 98. genomic context
  73. 99. Korbel et al., Nature Biotechnology , 2004
  74. 100. literature mining
  75. 102. curated knowledge
  76. 103. Letunic & Bork, Trends in Biochemical Sciences , 2008
  77. 104. different formats
  78. 105. different identifiers
  79. 106. different reliability
  80. 107. benchmarking
  81. 108. von Mering et al., Nucleic Acids Research , 2005
  82. 109. 373 genomes
  83. 110. Jensen et al., Nucleic Acids Research , 2008
  84. 111. transfer by orthology
  85. 112. combine all evidence
  86. 114. Acknowledgments <ul><li>Monica Campillos </li></ul><ul><li>Michael Kuhn </li></ul><ul><li>Christian von Mering </li></ul><ul><li>Anne-Claude Gavin </li></ul><ul><li>Peer Bork </li></ul>
  87. 115. http://larsjuhljensen.wordpress.com

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