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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. sequence analysis
  3. 3. protein networks
  4. 4. de Lichtenberg, Jensen et al., Science, 2005
  5. 5. adverse drug reactions
  6. 6. Campillos, Kuhn et al., Science, 2008
  7. 7. group leader
  8. 8. cofounder
  9. 9. data mining
  10. 10. proteomics
  11. 11. text mining
  12. 12. biomedical literature
  13. 13. electronic health records
  14. 14. protein networks
  15. 15. guilt by association
  16. 16. STRING
  17. 17. computational predictions
  18. 18. gene fusion
  19. 19. Korbel et al., Nature Biotechnology, 2004
  20. 20. gene neighborhood
  21. 21. Korbel et al., Nature Biotechnology, 2004
  22. 22. phylogenetic profiles
  23. 23. Korbel et al., Nature Biotechnology, 2004
  24. 24. experimental data
  25. 25. gene coexpression
  26. 26. protein interactions
  27. 27. Jensen & Bork, Science, 2008
  28. 28. curated knowledge
  29. 29. complexes
  30. 30. pathways
  31. 31. Letunic & Bork, Trends in Biochemical Sciences, 2008
  32. 32. many databases
  33. 33. different formats
  34. 34. different identifiers
  35. 35. variable quality
  36. 36. not comparable
  37. 37. hard work
  38. 38. quality scores
  39. 39. von Mering et al., Nucleic Acids Research, 2005
  40. 40. calibrate vs. gold standard
  41. 41. missing most of the data
  42. 42. text mining
  43. 43. >10 km
  44. 44. too much to read
  45. 45. computer
  46. 46. as smart as a dog
  47. 47. teach it specific tricks
  48. 48. named entity recognition
  49. 49. comprehensive lexicon
  50. 50. CDC2
  51. 51. cyclin dependent kinase 1
  52. 52. expansion rules
  53. 53. hCdc2
  54. 54. CDC2
  55. 55. flexible matching
  56. 56. cyclin-dependent kinase 1
  57. 57. cyclin dependent kinase 1
  58. 58. “black list”
  59. 59. SDS
  60. 60. augmented browsing
  61. 61. Reflect
  62. 62. browser add-on
  63. 63. real-time text mining
  64. 64. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010
  65. 65. information extraction
  66. 66. co-mentioning
  67. 67. within documents
  68. 68. within paragraphs
  69. 69. within sentences
  70. 70. text corpus
  71. 71. ~22 million abstracts
  72. 72. no access
  73. 73. ~4 million full-text articles
  74. 74. localization and disease
  75. 75. general approach
  76. 76. COMPARTMENTS
  77. 77. TISSUES
  78. 78. DISEASES
  79. 79. curated knowledge
  80. 80. experimental data
  81. 81. text mining
  82. 82. computational predictions
  83. 83. common identifiers
  84. 84. quality scores
  85. 85. visualization
  86. 86. compartments.jensenlab.org
  87. 87. tissues.jensenlab.org
  88. 88. dissemination
  89. 89. web interfaces
  90. 90. web services
  91. 91. diseases.jensenlab.org
  92. 92. bulk download
  93. 93. disease networks
  94. 94. medical data
  95. 95. electronic health records
  96. 96. central registries
  97. 97. individual hospitals
  98. 98. Jensen et al., Nature Reviews Genetics, 2012
  99. 99. structured data
  100. 100. Jensen et al., Nature Reviews Genetics, 2012
  101. 101. unstructured data
  102. 102. in Danish
  103. 103. by busy doctors
  104. 104. confounding factors
  105. 105. age and gender
  106. 106. reporting bias
  107. 107. custom dictionaries
  108. 108. typo rules
  109. 109. age/gender matching
  110. 110. comorbidity
  111. 111. Jensen et al., Nature Reviews Genetics, 2012
  112. 112. Roque et al., PLOS Computational Biology, 2011
  113. 113. temporal correlation
  114. 114. diagnosis trajectories
  115. 115. Jensen et al., in preparation, 2013
  116. 116. pharmocovigilance
  117. 117. adverse drug reactions
  118. 118. Eriksson et al., submitted, 2013
  119. 119. ADR profiles
  120. 120. Eriksson et al., submitted, 2013
  121. 121. ADR frequencies
  122. 122. Eriksson et al., submitted, 2013
  123. 123. molecular basis
  124. 124. protein networks
  125. 125. 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 Kalliopi Tsafou Alberto Santos 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
  126. 126. Thank you!

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