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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

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

Published in: Technology, Education
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  • 1. Prediction of protein–small molecule networks through large-scale data integration Lars Juhl Jensen
  • 2.  
  • 3. function prediction
  • 4.  
  • 5.  
  • 6. cell-cycle regulation
  • 7. de Lichtenberg & Jensen et al., Science , 2005
  • 8. data integration
  • 9.  
  • 10.  
  • 11. the problem
  • 12. new uses for old drugs
  • 13. drug–drug network
  • 14. shared target(s)
  • 15. chemical similarity
  • 16. Tanimoto coefficients
  • 17. Campillos & Kuhn et al., Science , 2008
  • 18. Campillos & Kuhn et al., Science , 2008
  • 19. similar drugs share targets
  • 20. only trivial predictions
  • 21. the idea
  • 22. chemical perturbations
  • 23. phenotypic readouts
  • 24. drug treatment
  • 25. side effects
  • 26. the implementation
  • 27. information on side effects
  • 28. package inserts
  • 29. Campillos & Kuhn et al., Science , 2008
  • 30. text mining
  • 31. side-effect ontology
  • 32. backtracking
  • 33. Campillos & Kuhn et al., Science , 2008
  • 34. side-effect correlations
  • 35. Campillos & Kuhn et al., Science , 2008
  • 36. GSC weighting
  • 37. side-effect frequencies
  • 38. Campillos & Kuhn et al., Science , 2008
  • 39. raw similarity score
  • 40. Campillos & Kuhn et al., Science , 2008
  • 41. p-values
  • 42. Campillos & Kuhn et al., Science , 2008
  • 43. side-effect similarity
  • 44. chemical similarity
  • 45. Campillos & Kuhn et al., Science , 2008
  • 46. reference set
  • 47. drug–target pairs
  • 48. Campillos & Kuhn et al., Science , 2008
  • 49. drug–drug pairs
  • 50. score bins
  • 51. benchmark
  • 52. Campillos & Kuhn et al., Science , 2008
  • 53. fit calibration function
  • 54. Campillos & Kuhn et al., Science , 2008
  • 55. probabilistic scores
  • 56. the results
  • 57. drug–drug network
  • 58. ATC codes
  • 59. Campillos & Kuhn et al., Science , 2008
  • 60. categorization
  • 61. Campillos & Kuhn et al., Science , 2008
  • 62. Campillos & Kuhn et al., Science , 2008
  • 63. Campillos & Kuhn et al., Science , 2008
  • 64. map onto score space
  • 65. Campillos & Kuhn et al., Science , 2008
  • 66. the experiments
  • 67. 20 drug–drug relations
  • 68. in vitro binding assays
  • 69. Campillos & Kuhn et al., Science , 2008
  • 70. Campillos & Kuhn et al., Science , 2008
  • 71. Campillos & Kuhn et al., Science , 2008
  • 72. K i <10 µM for 11 of 20
  • 73. cell assays
  • 74. Campillos & Kuhn et al., Science , 2008
  • 75. 9 of 9 showed activity
  • 76. the bigger picture
  • 77. STITCH
  • 78.  
  • 79. protein–chemical network
  • 80. Kuhn et al., Nucleic Acids Research , 2008
  • 81. primary experimental data
  • 82. activity screens
  • 83. Fedorov et al., PNAS , 2007
  • 84. protein interactions
  • 85. Jensen & Bork, Science , 2008
  • 86. gene coexpression
  • 87.  
  • 88. genomic context
  • 89. Korbel et al., Nature Biotechnology , 2004
  • 90. literature mining
  • 91.  
  • 92. curated knowledge
  • 93. Letunic & Bork, Trends in Biochemical Sciences , 2008
  • 94. different formats
  • 95. different identifiers
  • 96. different reliability
  • 97. benchmarking
  • 98. von Mering et al., Nucleic Acids Research , 2005
  • 99. 373 genomes
  • 100. Jensen et al., Nucleic Acids Research , 2008
  • 101. transfer by orthology
  • 102. combine all evidence
  • 103. Kuhn et al., Nucleic Acids Research , 2008
  • 104. Acknowledgments
    • Monica Campillos
    • Michael Kuhn
    • Christian von Mering
    • Anne-Claude Gavin
    • Peer Bork
  • 105.  

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