Information integration

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Information integration

  1. 1. Information integration Lars Juhl Jensen
  2. 2. Part 1the eukaryotic cell cycle
  3. 3. essential process
  4. 4. grow and divide
  5. 5. one cell
  6. 6. two cells
  7. 7. four phases
  8. 8. G1 phase
  9. 9. growth
  10. 10. S phase
  11. 11. DNA replication
  12. 12. G2 phase
  13. 13. growth
  14. 14. M phase
  15. 15. cell division
  16. 16. regulation
  17. 17. gene expression
  18. 18. phosphorylation
  19. 19. targeted degradation
  20. 20. protein interactions
  21. 21. Example 1my protein and friends
  22. 22. http://string-db.org
  23. 23. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
  24. 24. Part 2association networks
  25. 25. guild by association
  26. 26. STRING
  27. 27. >1100 genomes
  28. 28. genomic context
  29. 29. gene fusion
  30. 30. Korbel et al., Nature Biotechnology, 2004
  31. 31. conserved neighborhood
  32. 32. Korbel et al., Nature Biotechnology, 2004
  33. 33. phylogenetic profiles
  34. 34. Korbel et al., Nature Biotechnology, 2004
  35. 35. protein interactions
  36. 36. Jensen & Bork, Science, 2008
  37. 37. genetic interactions
  38. 38. Beyer et al., Nature Reviews Genetics, 2007
  39. 39. gene coexpression
  40. 40. curated knowledge
  41. 41. Letunic & Bork, Trends in Biochemical Sciences, 2008
  42. 42. >10 km
  43. 43. text mining
  44. 44. co-mentioning
  45. 45. NLPNatural Language Processing
  46. 46. Gene and protein namesCue words for entity recognitionVerbs for relation extraction[nxgene The GAL4 gene][nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]
  47. 47. different sources
  48. 48. different formats
  49. 49. different names
  50. 50. not comparable
  51. 51. variable quality
  52. 52. many parsers
  53. 53. comprehensive lexicon
  54. 54. quality scores
  55. 55. look at the data
  56. 56. von Mering et al., Nucleic Acids Research, 2005
  57. 57. scoring scheme
  58. 58. benchmark
  59. 59. von Mering et al., Nucleic Acids Research, 2005
  60. 60. probabilistic scores
  61. 61. combine scores
  62. 62. Example 2evidence filters and viewers
  63. 63. highest confidence only
  64. 64. experiments only
  65. 65. evidence viewers
  66. 66. Part 3analysis of cell-cycle data
  67. 67. gene expression
  68. 68. cell cultures
  69. 69. synchronization
  70. 70. microarrays
  71. 71. time courses
  72. 72. look at the data
  73. 73. Gauthier et al., Nucleic Acids Research, 2007
  74. 74. scoring scheme
  75. 75. benchmark
  76. 76. time of peak expression
  77. 77. protein interactions
  78. 78. temporal network
  79. 79. de Lichtenberg, Jensen et al., Science, 2005
  80. 80. Example 3a network for my proteins
  81. 81. http://string-db.org
  82. 82. high confidence only
  83. 83. experiments only
  84. 84. network expansion
  85. 85. Part 4external data
  86. 86. save network
  87. 87. open in Cytoscape
  88. 88. layout
  89. 89. clustering
  90. 90. project data onto network
  91. 91. de Lichtenberg, Jensen et al., Science, 2005
  92. 92. very flexible
  93. 93. lose the STRING interface
  94. 94. payload mechanism
  95. 95. show external data
  96. 96. nodes
  97. 97. edges
  98. 98. hosted on your server
  99. 99. Example 4my data in STRING
  100. 100. http://cyclebase-string.jensenlab.org
  101. 101. Conclusions
  102. 102. know your question
  103. 103. collect data
  104. 104. look at the data
  105. 105. benchmark
  106. 106. Thank you!
  107. 107. larsjuhljensen

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