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

    1. 1. Network biology A basis for large-scale biomedical data mining Lars Juhl Jensen
    2. 2. sequence analysis
    3. 3. Jensen, Gupta et al., Journal of Molecular Biology, 2002
    4. 4. data mining
    5. 5. de Lichtenberg, Jensen et al., Science, 2005
    6. 6. data mining
    7. 7. text mining
    8. 8. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
    9. 9. signaling networks
    10. 10. phosphoproteomics
    11. 11. in vivo phosphosites
    12. 12. kinases are unknown
    13. 13. sequence motifs
    14. 14. Miller, Jensen et al., Science Signaling, 2008
    15. 15. NetPhorest
    16. 16. data organization
    17. 17. Miller, Jensen et al., Science Signaling, 2008
    18. 18. automated pipeline
    19. 19. Miller, Jensen et al., Science Signaling, 2008
    20. 20. compilation of datasets
    21. 21. training and evaluation
    22. 22. motif atlas
    23. 23. 179 kinases
    24. 24. 89 SH2 domains
    25. 25. 8 PTB domains
    26. 26. BRCT domains
    27. 27. WW domains
    28. 28. 14-3-3 proteins
    29. 29. phosphatases
    30. 30. sequence specificity
    31. 31. in vitro
    32. 32. network context
    33. 33. Linding, Jensen, Ostheimer et al., Cell, 2007
    34. 34. STRING
    35. 35. Jensen, Kuhn et al., Nucleic Acids Research, 2009
    36. 36. 630 genomes
    37. 37. 2.5 million proteins
    38. 38. genomic context
    39. 39. gene fusion
    40. 40. Korbel et al., Nature Biotechnology, 2004
    41. 41. phylogenetic profiles
    42. 42. Korbel et al., Nature Biotechnology, 2004
    43. 43. primary experimental data
    44. 44. physical interactions
    45. 45. Jensen & Bork, Science, 2008
    46. 46. gene coexpression
    47. 47. curated knowledge
    48. 48. Letunic & Bork, Trends in Biochemical Sciences, 2008
    49. 49. literature mining
    50. 50. not comparable
    51. 51. confidence scores
    52. 52. von Mering et al., Nucleic Acids Research, 2005
    53. 53. cross-species integration
    54. 54. Linding, Jensen, Ostheimer et al., Cell, 2007
    55. 55. putting it all together
    56. 56. NetworKIN
    57. 57. Linding, Jensen, Ostheimer et al., Cell, 2007
    58. 58. >2x better accuracy
    59. 59. use case
    60. 60. DNA damage response
    61. 61. Linding, Jensen, Ostheimer et al., Cell, 2007
    62. 62. experimental validation
    63. 63. ATM phosphorylates Rad50
    64. 64. Linding, Jensen, Ostheimer et al., Cell, 2007
    65. 65. drug repositioning
    66. 66. new uses for old drugs
    67. 67. drug–drug network
    68. 68. shared target(s)
    69. 69. chemical similarity
    70. 70. Tanimoto coefficients
    71. 71. Campillos & Kuhn et al., Science, 2008
    72. 72. Campillos & Kuhn et al., Science, 2008
    73. 73. similar drugs share targets
    74. 74. only trivial predictions
    75. 75. phenotypic similarity
    76. 76. chemical perturbations
    77. 77. phenotypic readouts
    78. 78. drug treatment
    79. 79. side effects
    80. 80. no database
    81. 81. package inserts
    82. 82. Campillos & Kuhn et al., Science, 2008
    83. 83. text mining
    84. 84. side-effect ontology
    85. 85. backtracking
    86. 86. Campillos & Kuhn et al., Science, 2008
    87. 87. side-effect correlations
    88. 88. Campillos & Kuhn et al., Science, 2008
    89. 89. GSC weighting
    90. 90. side-effect frequencies
    91. 91. Campillos & Kuhn et al., Science, 2008
    92. 92. raw similarity score
    93. 93. Campillos & Kuhn et al., Science, 2008
    94. 94. p-values
    95. 95. Campillos & Kuhn et al., Science, 2008
    96. 96. side-effect similarity
    97. 97. chemical similarity
    98. 98. Campillos & Kuhn et al., Science, 2008
    99. 99. confidence scores
    100. 100. drug–drug network
    101. 101. Campillos & Kuhn et al., Science, 2008
    102. 102. categorization
    103. 103. Campillos & Kuhn et al., Science, 2008
    104. 104. experimental validation
    105. 105. 20 drug–drug pairs
    106. 106. in vitro binding assays
    107. 107. Ki<10 µM for 11 of 20
    108. 108. cell assays
    109. 109. 9 of 9 showed activity
    110. 110. work in progress
    111. 111. link side-effects to targets
    112. 112. direct target prediction
    113. 113. STITCH
    114. 114. Kuhn et al., Nucleic Acids Research, 2010
    115. 115. thank you!
    116. 116. 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
    117. 117. larsjuhljensen

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