Network biology: Large-scale biomedical data and text mining

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  • Integration Automation Collaboration
  • Network biology: Large-scale biomedical data and text mining

    1. 1. Network biology Large-scale biomedical data and text mining Lars Juhl Jensen
    2. 2. three parts
    3. 3. association networks
    4. 4. signaling networks
    5. 5. drug networks
    6. 6. Part 1 association networks
    7. 7. guilt by association
    8. 9. STRING
    9. 10. Szklarczyk, Franceschini et al., Nucleic Acids Research , 2011
    10. 11. >1100 genomes
    11. 12. genomic context
    12. 13. gene fusion
    13. 14. Korbel et al., Nature Biotechnology , 2004
    14. 15. experimental data
    15. 16. protein interactions
    16. 17. Jensen & Bork, Science , 2008
    17. 18. curated knowledge
    18. 19. pathways
    19. 20. Letunic & Bork, Trends in Biochemical Sciences , 2008
    20. 21. many data types
    21. 22. many databases
    22. 23. different formats
    23. 24. different identifiers
    24. 25. variable quality
    25. 26. quality scores
    26. 27. von Mering et al., Nucleic Acids Research , 2005
    27. 28. calibrate vs. gold standard
    28. 29. von Mering et al., Nucleic Acids Research , 2005
    29. 30. orthology transfer
    30. 31. missing most of the data
    31. 32. >10 km
    32. 33. too much to read
    33. 34. computer
    34. 35. as smart as a dog
    35. 36. teach it specific tricks
    36. 39. named entity recognition
    37. 40. identify the concepts
    38. 41. proteins
    39. 42. comprehensive lexicon
    40. 43. orthographic variation
    41. 44. “ black list”
    42. 45. Reflect
    43. 46. augmented browsing
    44. 47. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009 O’Donoghue et al., Journal of Web Semantics , 2010
    45. 48. information extraction
    46. 49. co-mentioning
    47. 51. Part 2 signaling networks
    48. 52. phosphoproteomics
    49. 53. in vivo phosphosites
    50. 54. kinases are unknown
    51. 55. sequence specificity
    52. 56. Miller, Jensen et al., Science Signaling , 2008
    53. 57. NetPhorest
    54. 58. automated pipeline
    55. 59. Miller, Jensen et al., Science Signaling , 2008
    56. 60. protein-specific
    57. 61. no context
    58. 62. co-activators
    59. 63. protein scaffolds
    60. 64. localization
    61. 65. expression
    62. 66. association network
    63. 67. Linding, Jensen, Ostheimer et al., Cell , 2007
    64. 68. NetworKIN
    65. 69. Linding, Jensen, Ostheimer et al., Cell , 2007
    66. 71. Part 3 drug networks
    67. 72. drug repurposing
    68. 73. drug–drug network
    69. 74. chemical similarity
    70. 75. Campillos & Kuhn et al., Science , 2008
    71. 76. only trivial predictions
    72. 77. phenotypic similarity
    73. 78. chemical perturbations
    74. 79. phenotypic readouts
    75. 80. drug treatment
    76. 81. side effects
    77. 82. no database
    78. 83. package inserts
    79. 84. Campillos & Kuhn et al., Science , 2008
    80. 85. text mining
    81. 86. manual validation
    82. 87. SIDER
    83. 88. side-effect similarity
    84. 89. Campillos & Kuhn et al., Science , 2008
    85. 90. combined similarity
    86. 91. Campillos & Kuhn et al., Science , 2008
    87. 92. categorization
    88. 93. Campillos & Kuhn et al., Science , 2008
    89. 94. 20 drug–drug pairs
    90. 95. in vitro binding assays
    91. 96. K i <10 µM for 11 of 20
    92. 97. cell assays
    93. 98. 9 of 9 showed activity
    94. 99. Acknowledgments <ul><ul><li>Reflect </li></ul></ul><ul><ul><li>Sune Frankild </li></ul></ul><ul><ul><li>Heiko Horn </li></ul></ul><ul><ul><li>Evangelos Pafilis </li></ul></ul><ul><ul><li>Michael Kuhn </li></ul></ul><ul><ul><li>Reinhardt Schneider </li></ul></ul><ul><ul><li>Sean O’Donoghue </li></ul></ul><ul><ul><li>Side effects </li></ul></ul><ul><ul><li>Monica Campillos </li></ul></ul><ul><ul><li>Michael Kuhn </li></ul></ul><ul><ul><li>Anne-Claude Gavin </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><ul><li>STRING </li></ul></ul><ul><ul><li>Damian Szklarczyk </li></ul></ul><ul><ul><li>Andrea Franceschini </li></ul></ul><ul><ul><li>Michael Kuhn </li></ul></ul><ul><ul><li>Milan Simonovic </li></ul></ul><ul><ul><li>Alexander Roth </li></ul></ul><ul><ul><li>Pablo Minguez </li></ul></ul><ul><ul><li>Tobias Doerks </li></ul></ul><ul><ul><li>Manuel Stark </li></ul></ul><ul><ul><li>Jean Muller </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><ul><li>Christian von Mering </li></ul></ul><ul><ul><li>NetworKIN </li></ul></ul><ul><ul><li>Heiko Horn </li></ul></ul><ul><ul><li>Martin Lee Miller </li></ul></ul><ul><ul><li>Gerard Ostheimer </li></ul></ul><ul><ul><li>Francesca Diella </li></ul></ul><ul><ul><li>Claus Jørgensen </li></ul></ul><ul><ul><li>Rob Russell </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><ul><li>Benjamin Turk </li></ul></ul><ul><ul><li>Michael Yaffe </li></ul></ul><ul><ul><li>Tony Pawson </li></ul></ul><ul><ul><li>Rune Linding </li></ul></ul>
    95. 100. larsjuhljensen

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