Prediction of protein–small molecule networks through large-scale data integration Lars Juhl Jensen
 
function prediction
 
 
cell-cycle regulation
de  Lichtenberg & Jensen et al.,  Science , 2005
data integration
 
 
the problem
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
the idea
chemical perturbations
phenotypic readouts
drug treatment
side effects
the implementation
information on side effects
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
reference set
drug–target pairs
Campillos & Kuhn et al.,  Science , 2008
drug–drug pairs
score bins
benchmark
Campillos & Kuhn et al.,  Science , 2008
fit calibration function
Campillos & Kuhn et al.,  Science , 2008
probabilistic scores
the results
drug–drug network
ATC codes
Campillos & Kuhn et al.,  Science , 2008
categorization
Campillos & Kuhn et al.,  Science , 2008
Campillos & Kuhn et al.,  Science , 2008
Campillos & Kuhn et al.,  Science , 2008
map onto score space
Campillos & Kuhn et al.,  Science , 2008
the experiments
20 drug–drug relations
in vitro  binding assays
Campillos & Kuhn et al.,  Science , 2008
Campillos & Kuhn et al.,  Science , 2008
Campillos & Kuhn et al.,  Science , 2008
K i <10 µM for 11 of 20
cell assays
Campillos & Kuhn et al.,  Science , 2008
9 of 9 showed activity
the bigger picture
STITCH
 
protein–chemical network
Kuhn et al.,  Nucleic Acids Research , 2008
primary experimental data
activity screens
Fedorov et al.,  PNAS , 2007
protein interactions
Jensen & Bork,  Science , 2008
gene coexpression
 
genomic context
Korbel et al.,  Nature Biotechnology , 2004
literature mining
 
curated knowledge
Letunic & Bork,  Trends in Biochemical Sciences , 2008
different formats
different identifiers
different reliability
benchmarking
von Mering et al.,  Nucleic Acids Research , 2005
373 genomes
Jensen et al.,  Nucleic Acids Research , 2008
transfer by orthology
combine all evidence
Kuhn et al.,  Nucleic Acids Research , 2008
Acknowledgments <ul><li>Monica Campillos </li></ul><ul><li>Michael Kuhn </li></ul><ul><li>Christian von Mering </li></ul><...
 
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Prediction of protein-small molecule networks through large-scale data integration

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KU Bioinformatics Workshop, University of Copenhagen, Copenhagen, Denmark, January 26, 2009

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Prediction of protein-small molecule networks through large-scale data integration

  1. 1. Prediction of protein–small molecule networks through large-scale data integration Lars Juhl Jensen
  2. 3. function prediction
  3. 6. cell-cycle regulation
  4. 7. de Lichtenberg & Jensen et al., Science , 2005
  5. 8. data integration
  6. 11. the problem
  7. 12. new uses for old drugs
  8. 13. drug–drug network
  9. 14. shared target(s)
  10. 15. chemical similarity
  11. 16. Tanimoto coefficients
  12. 17. Campillos & Kuhn et al., Science , 2008
  13. 18. Campillos & Kuhn et al., Science , 2008
  14. 19. similar drugs share targets
  15. 20. only trivial predictions
  16. 21. the idea
  17. 22. chemical perturbations
  18. 23. phenotypic readouts
  19. 24. drug treatment
  20. 25. side effects
  21. 26. the implementation
  22. 27. information on side effects
  23. 28. package inserts
  24. 29. Campillos & Kuhn et al., Science , 2008
  25. 30. text mining
  26. 31. side-effect ontology
  27. 32. backtracking
  28. 33. Campillos & Kuhn et al., Science , 2008
  29. 34. side-effect correlations
  30. 35. Campillos & Kuhn et al., Science , 2008
  31. 36. GSC weighting
  32. 37. side-effect frequencies
  33. 38. Campillos & Kuhn et al., Science , 2008
  34. 39. raw similarity score
  35. 40. Campillos & Kuhn et al., Science , 2008
  36. 41. p-values
  37. 42. Campillos & Kuhn et al., Science , 2008
  38. 43. side-effect similarity
  39. 44. chemical similarity
  40. 45. Campillos & Kuhn et al., Science , 2008
  41. 46. reference set
  42. 47. drug–target pairs
  43. 48. Campillos & Kuhn et al., Science , 2008
  44. 49. drug–drug pairs
  45. 50. score bins
  46. 51. benchmark
  47. 52. Campillos & Kuhn et al., Science , 2008
  48. 53. fit calibration function
  49. 54. Campillos & Kuhn et al., Science , 2008
  50. 55. probabilistic scores
  51. 56. the results
  52. 57. drug–drug network
  53. 58. ATC codes
  54. 59. Campillos & Kuhn et al., Science , 2008
  55. 60. categorization
  56. 61. Campillos & Kuhn et al., Science , 2008
  57. 62. Campillos & Kuhn et al., Science , 2008
  58. 63. Campillos & Kuhn et al., Science , 2008
  59. 64. map onto score space
  60. 65. Campillos & Kuhn et al., Science , 2008
  61. 66. the experiments
  62. 67. 20 drug–drug relations
  63. 68. in vitro binding assays
  64. 69. Campillos & Kuhn et al., Science , 2008
  65. 70. Campillos & Kuhn et al., Science , 2008
  66. 71. Campillos & Kuhn et al., Science , 2008
  67. 72. K i <10 µM for 11 of 20
  68. 73. cell assays
  69. 74. Campillos & Kuhn et al., Science , 2008
  70. 75. 9 of 9 showed activity
  71. 76. the bigger picture
  72. 77. STITCH
  73. 79. protein–chemical network
  74. 80. Kuhn et al., Nucleic Acids Research , 2008
  75. 81. primary experimental data
  76. 82. activity screens
  77. 83. Fedorov et al., PNAS , 2007
  78. 84. protein interactions
  79. 85. Jensen & Bork, Science , 2008
  80. 86. gene coexpression
  81. 88. genomic context
  82. 89. Korbel et al., Nature Biotechnology , 2004
  83. 90. literature mining
  84. 92. curated knowledge
  85. 93. Letunic & Bork, Trends in Biochemical Sciences , 2008
  86. 94. different formats
  87. 95. different identifiers
  88. 96. different reliability
  89. 97. benchmarking
  90. 98. von Mering et al., Nucleic Acids Research , 2005
  91. 99. 373 genomes
  92. 100. Jensen et al., Nucleic Acids Research , 2008
  93. 101. transfer by orthology
  94. 102. combine all evidence
  95. 103. Kuhn et al., Nucleic Acids Research , 2008
  96. 104. 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>

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