Networks of proteins and diseases

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Networks of proteins and diseases

  1. 1. Networks of proteins and diseases Lars Juhl Jensen
  2. 2. association networks
  3. 3. proteins
  4. 4. diseases
  5. 5. protein networks
  6. 6. STRING
  7. 7. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
  8. 8. computational predictions
  9. 9. gene fusion
  10. 10. Korbel et al., Nature Biotechnology, 2004
  11. 11. conserved neighborhood
  12. 12. Korbel et al., Nature Biotechnology, 2004
  13. 13. experimental data
  14. 14. physical interactions
  15. 15. Jensen & Bork, Science, 2008
  16. 16. curated knowledge
  17. 17. metabolic pathways
  18. 18. Letunic & Bork, Trends in Biochemical Sciences, 2008
  19. 19. integrate it all
  20. 20. many databases
  21. 21. different formats
  22. 22. different identifiers
  23. 23. variable quality
  24. 24. not comparable
  25. 25. hard work
  26. 26. quality scores
  27. 27. von Mering et al., Nucleic Acids Research, 2005
  28. 28. calibrate vs. gold standard
  29. 29. missing most of the data
  30. 30. text mining
  31. 31. >10 km
  32. 32. too much to read
  33. 33. computer
  34. 34. as smart as a dog
  35. 35. teach it specific tricks
  36. 36. named entity recognition
  37. 37. comprehensive lexicon
  38. 38. cyclin dependent kinase 1
  39. 39. CDC2
  40. 40. expansion rules
  41. 41. CDC2
  42. 42. hCdc2
  43. 43. flexible matching
  44. 44. cyclin dependent kinase 1
  45. 45. cyclin-dependent kinase 1
  46. 46. “black list”
  47. 47. SDS
  48. 48. co-mentioning
  49. 49. within documents
  50. 50. within paragraphs
  51. 51. within sentences
  52. 52. weighted counts
  53. 53. localization and disease
  54. 54. general approach
  55. 55. suite of web resources
  56. 56. curated knowledge
  57. 57. experimental data
  58. 58. text mining
  59. 59. computational predictions
  60. 60. quality scores
  61. 61. proteins
  62. 62. compartments
  63. 63. compartments.jensenlab.org
  64. 64. tissues
  65. 65. tissues.jensenlab.org
  66. 66. diseases
  67. 67. evidence viewers
  68. 68. web services
  69. 69. compartments.jensenlab.org
  70. 70. download files
  71. 71. disease networks
  72. 72. electronic health records
  73. 73. Jensen et al., Nature Reviews Genetics, 2012
  74. 74. structured data
  75. 75. Jensen et al., Nature Reviews Genetics, 2012
  76. 76. unstructured data
  77. 77. comorbidity
  78. 78. Jensen et al., Nature Reviews Genetics, 2012
  79. 79. Roque et al., PLoS Computational Biology, 2011
  80. 80. in Danish
  81. 81. multiple testing
  82. 82. confounding factors
  83. 83. age and gender
  84. 84. reporting bias
  85. 85. temporal correlation
  86. 86. diagnosis trajectories
  87. 87. Jensen et al., in preparation, 2013
  88. 88. diabetes progression
  89. 89. Jensen et al., in preparation, 2013
  90. 90. molecular basis
  91. 91. protein networks
  92. 92. Acknowledgments STRING 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 Text mining Sune Frankild Evangelos Pafilis Alberto Santos Kalliopi Tsafou Janos Binder Heiko Horn Michael Kuhn Nigel Brown Reinhardt Schneider Sean O’Donoghue EHR mining Anders Boeck Jensen Peter Bjødstrup Jensen Francisco S. Roque Henriette Schmock Marlene Dalgaard Massimo Andreatta Thomas Hansen Karen Søeby Søren Bredkjær Anders Juul Tudor Oprea Pope Moseley Thomas Werge Søren Brunak

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