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Network biology: A basis for large-scale biomedical data mining

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  • Integration Automation Collaboration
  • Atlas of human kinases Atlases for phospho-binding proteins Atlases for model organisms Ubiquitination would be welcome

Network biology: A basis for large-scale biomedical data mining Presentation Transcript

  • 1. Network biology A basis for large-scale biomedical data mining Lars Juhl Jensen
  • 2.  
  • 3.  
  • 4. sequence analysis
  • 5. Jensen, Gupta et al., Journal of Molecular Biology , 2002
  • 6.  
  • 7.  
  • 8. data mining
  • 9. de Lichtenberg, Jensen et al., Science , 2005
  • 10.  
  • 11.  
  • 12.  
  • 13. data mining
  • 14. text mining
  • 15. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009
  • 16. signaling networks
  • 17. phosphoproteomics
  • 18.  
  • 19. in vivo phosphosites
  • 20. kinases are unknown
  • 21. sequence motifs
  • 22. Miller, Jensen et al., Science Signaling , 2008
  • 23. NetPhorest
  • 24. data organization
  • 25. Miller, Jensen et al., Science Signaling , 2008
  • 26. automated pipeline
  • 27. Miller, Jensen et al., Science Signaling , 2008
  • 28. compilation of datasets
  • 29. training and evaluation
  • 30. motif atlas
  • 31.  
  • 32. 179 kinases
  • 33. 89 SH2 domains
  • 34. 8 PTB domains
  • 35. BRCT domains
  • 36. WW domains
  • 37. 14-3-3 proteins
  • 38. phosphatases
  • 39. sequence specificity
  • 40. in vitro
  • 41. network context
  • 42. Linding, Jensen, Ostheimer et al., Cell , 2007
  • 43. STRING
  • 44. Jensen, Kuhn et al., Nucleic Acids Research , 2009
  • 45. 630 genomes
  • 46. 2.5 million proteins
  • 47. genomic context
  • 48. gene fusion
  • 49. Korbel et al., Nature Biotechnology , 2004
  • 50. phylogenetic profiles
  • 51. Korbel et al., Nature Biotechnology , 2004
  • 52. primary experimental data
  • 53. physical interactions
  • 54. Jensen & Bork, Science , 2008
  • 55. gene coexpression
  • 56.  
  • 57. curated knowledge
  • 58. Letunic & Bork, Trends in Biochemical Sciences , 2008
  • 59. literature mining
  • 60.  
  • 61. not comparable
  • 62. confidence scores
  • 63. von Mering et al., Nucleic Acids Research , 2005
  • 64. cross-species integration
  • 65. Linding, Jensen, Ostheimer et al., Cell , 2007
  • 66. putting it all together
  • 67. NetworKIN
  • 68. Linding, Jensen, Ostheimer et al., Cell , 2007
  • 69. >2x better accuracy
  • 70. use case
  • 71. DNA damage response
  • 72. Linding, Jensen, Ostheimer et al., Cell , 2007
  • 73. experimental validation
  • 74. ATM phosphorylates Rad50
  • 75. Linding, Jensen, Ostheimer et al., Cell , 2007
  • 76. drug repositioning
  • 77. new uses for old drugs
  • 78. drug–drug network
  • 79. shared target(s)
  • 80. chemical similarity
  • 81. Tanimoto coefficients
  • 82. Campillos & Kuhn et al., Science , 2008
  • 83. Campillos & Kuhn et al., Science , 2008
  • 84. similar drugs share targets
  • 85. only trivial predictions
  • 86. phenotypic similarity
  • 87. chemical perturbations
  • 88. phenotypic readouts
  • 89. drug treatment
  • 90. side effects
  • 91. no database
  • 92. package inserts
  • 93. Campillos & Kuhn et al., Science , 2008
  • 94. text mining
  • 95. side-effect ontology
  • 96. backtracking
  • 97. Campillos & Kuhn et al., Science , 2008
  • 98. side-effect correlations
  • 99. Campillos & Kuhn et al., Science , 2008
  • 100. GSC weighting
  • 101. side-effect frequencies
  • 102. Campillos & Kuhn et al., Science , 2008
  • 103. raw similarity score
  • 104. Campillos & Kuhn et al., Science , 2008
  • 105. p-values
  • 106. Campillos & Kuhn et al., Science , 2008
  • 107. side-effect similarity
  • 108. chemical similarity
  • 109. Campillos & Kuhn et al., Science , 2008
  • 110. confidence scores
  • 111. drug–drug network
  • 112. Campillos & Kuhn et al., Science , 2008
  • 113. categorization
  • 114. Campillos & Kuhn et al., Science , 2008
  • 115. experimental validation
  • 116. 20 drug–drug pairs
  • 117. in vitro binding assays
  • 118. K i <10 µM for 11 of 20
  • 119. cell assays
  • 120. 9 of 9 showed activity
  • 121. work in progress
  • 122. link side-effects to targets
  • 123. direct target prediction
  • 124. STITCH
  • 125. Kuhn et al., Nucleic Acids Research , 2010
  • 126. thank you!
  • 127. Acknowledgments
    • NetPhorest.info
      • Rune Linding
      • Martin Lee Miller
      • Francesca Diella
      • Claus Jørgensen
      • Michele Tinti
      • Lei Li
      • Marilyn Hsiung
      • Sirlester A. Parker
      • Jennifer Bordeaux
      • Thomas Sicheritz-Pontén
      • Marina Olhovsky
      • Adrian Pasculescu
      • Jes Alexander
      • Stefan Knapp
      • Nikolaj Blom
      • Peer Bork
      • Shawn Li
      • Gianni Cesareni
      • Tony Pawson
      • Benjamin E. Turk
      • Michael B. Yaffe
      • Søren Brunak
    • STRING-DB.org
      • Christian von Mering
      • Damian Szklarczyk
      • Michael Kuhn
      • Manuel Stark
      • Samuel Chaffron
      • Chris Creevey
      • Jean Muller
      • Tobias Doerks
      • Philippe Julien
      • Alexander Roth
      • Milan Simonovic
      • Jan Korbel
      • Berend Snel
      • Martijn Huynen
      • Peer Bork
    • Side effect
      • Monica Campillos
      • Michael Kuhn
      • Christian von Mering
      • Anne-Claude Gavin
      • Peer Bork
    • NetworKIN.info
      • Rune Linding
      • Gerard Ostheimer
      • Heiko Horn
      • Martin Lee Miller
      • Francesca Diella
      • Karen Colwill
      • Jing Jin
      • Pavel Metalnikov
      • Vivian Nguyen
      • Adrian Pasculescu
      • Jin Gyoon Park
      • Leona D. Samson
      • Rob Russell
      • Peer Bork
      • Michael Yaffe
      • Tony Pawson
  • 128. larsjuhljensen