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Network biology

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  • Integration Automation Collaboration
  • Atlas of human kinases Atlases for phospho-binding proteins Atlases for model organisms Ubiquitination would be welcome
  • Transcript

    • 1. Network biology Lars Juhl Jensen
    • 2.  
    • 3.  
    • 4. sequence analysis
    • 5. Jensen, Gupta et al., Journal of Molecular Biology , 2002
    • 6.  
    • 7.  
    • 8. data mining
    • 9. de Lichtenberg, Jensen et al., Science , 2005
    • 10. Campillos & Kuhn et al., Science , 2008
    • 11.  
    • 12.  
    • 13.  
    • 14. medical informatics
    • 15.  
    • 16. Part 1 signaling networks
    • 17. phosphoproteomics
    • 18. Linding, Jensen, Ostheimer et al., Cell , 2007
    • 19. in vivo phosphosites
    • 20. kinases are unknown
    • 21. sequence specificity
    • 22. Miller, Jensen et al., Science Signaling , 2008
    • 23. NetPhorest
    • 24. data organization
    • 25. Miller, Jensen et al., Science Signaling , 2008
    • 26. automated pipeline
    • 27. Miller, Jensen et al., Science Signaling , 2008
    • 28. motif atlas
    • 29.  
    • 30. protein-specific
    • 31. no context
    • 32. co-activators
    • 33. protein scaffolds
    • 34. localization
    • 35. expression
    • 36. association network
    • 37. Linding, Jensen, Ostheimer et al., Cell , 2007
    • 38. NetworKIN
    • 39. Linding, Jensen, Ostheimer et al., Cell , 2007
    • 40.  
    • 41. Part 2 association networks
    • 42. guilt by association
    • 43.  
    • 44. STRING
    • 45. Szklarczyk, Franceschini et al., Nucleic Acids Research , 2011
    • 46. 630 genomes
    • 47. genomic context
    • 48. gene fusion
    • 49. Korbel et al., Nature Biotechnology , 2004
    • 50. phylogenetic profiles
    • 51. Korbel et al., Nature Biotechnology , 2004
    • 52. experimental data
    • 53. protein interactions
    • 54. Jensen & Bork, Science , 2008
    • 55. genetic interactions
    • 56. Beyer et al., Nature Reviews Genetics , 2007
    • 57. gene coexpression
    • 58.  
    • 59. curated knowledge
    • 60. protein complexes
    • 61. pathways
    • 62. Letunic & Bork, Trends in Biochemical Sciences , 2008
    • 63. many data types
    • 64. many databases
    • 65. different formats
    • 66. different identifiers
    • 67. variable quality
    • 68. many parsers
    • 69. identifier mapping
    • 70. quality scores
    • 71. calibrate vs. gold standard
    • 72. von Mering et al., Nucleic Acids Research , 2005
    • 73. orthology transfer
    • 74. von Mering et al., Nucleic Acids Research , 2005
    • 75. missing most of the data
    • 76. Part 3 text mining
    • 77. exponential growth
    • 78.  
    • 79.  
    • 80. some things are constant
    • 81.  
    • 82. ~45 seconds per paper
    • 83. computer
    • 84. as smart as a dog
    • 85. teach it specific tricks
    • 86.  
    • 87.  
    • 88. named entity identification
    • 89. Reflect
    • 90. augmented browsing
    • 91. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009
    • 92. comprehensive lexicon
    • 93. orthographic variation
    • 94. “ black list”
    • 95. information extraction
    • 96. co-occurrence
    • 97.  
    • 98. scalable
    • 99. >10 km <10 hours
    • 100. no access
    • 101.  
    • 102. collaborate with publishers
    • 103.  
    • 104. Acknowledgments
        • Reflect
        • Sune Frankild
        • Heiko Horn
        • Evangelos Pafilis
        • Michael Kuhn
        • Reinhardt Schneider
        • Sean O’Donoghue
        • STRING
        • Damian Szklarczyk
        • Andrea Franceschini
        • Michael Kuhn
        • Milan Simonovic
        • Alexander Roth
        • Pablo Minguez
        • Tobias Doerks
        • Manuel Stark
        • Jean Muller
        • Peer Bork
        • Christian von Mering
        • NetworKIN
        • Heiko Horn
        • Martin Lee Miller
        • Gerard Ostheimer
        • Francesca Diella
        • Claus Jørgensen
        • Rob Russell
        • Peer Bork
        • Benjamin Turk
        • Michael Yaffe
        • Tony Pawson
        • Rune Linding
    • 105. larsjuhljensen

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