Medical bioinformatics
Current trends, industry needs, and core competences
Lars Juhl Jensen
Ph.D.
postdoc
staff scientist
group leader
cofounder
current trends
compressive genomics
next generation sequencing
scaling challenge
Loh et al., Nature Biotechnology, 2012
exponential cost increase
store only differences
work on compressed data
sublinear scaling
new algorithms needed
network biology
beyond pathways
no discoveries
protein networks
STRING
Franceschini et al., Nucleic Acids Research, 2013
regulation
cell cycle
de Lichtenberg, Jensen et al., Science, 2005
disease phenotypes
heart development
Lage et al., Molecular Systems Biology, 2010
diabetes
Bergholdt et al., Diabetes, 2013
applied text mining
biomedical literature
>10 km
too much to read
full-text articles
electronic health records
structured data
Jensen et al., Nature Reviews Genetics, 2012
unstructured data
temporal correlations
diagnosis trajectories
Jensen et al., submitted, 2013
adverse drug reactions
Eriksson et al., submitted, 2013
industry needs
goals
biomarker discovery
target discovery
ADR prediction
data sources
in-house data
expression profiles
screening assays
security
legal agreements
poorly standardized
public data
expression profiles
interaction networks
literature
unawareness
patient data
security
privacy
core competencies
biological data mining
quantity is not the problem
many data types
different formats
different identifiers
different quality
scripting language
biological understanding
statistical mindset
medical text mining
free text
in Norwegian
by busy doctors
text-mining techniques
information extraction
named entity recognition
dictionary construction
medical knowledge
speak Norwegian
summary
custom solution
no standard software
interdisciplinary team
outsourcing
Medical bioinformatics: Current trends, industry needs, and core competencies
Medical bioinformatics: Current trends, industry needs, and core competencies
Medical bioinformatics: Current trends, industry needs, and core competencies
Medical bioinformatics: Current trends, industry needs, and core competencies
Medical bioinformatics: Current trends, industry needs, and core competencies
Medical bioinformatics: Current trends, industry needs, and core competencies
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Medical bioinformatics: Current trends, industry needs, and core competencies

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Medical bioinformatics: Current trends, industry needs, and core competencies

  1. 1. Medical bioinformatics Current trends, industry needs, and core competences Lars Juhl Jensen
  2. 2. Ph.D.
  3. 3. postdoc
  4. 4. staff scientist
  5. 5. group leader
  6. 6. cofounder
  7. 7. current trends
  8. 8. compressive genomics
  9. 9. next generation sequencing
  10. 10. scaling challenge
  11. 11. Loh et al., Nature Biotechnology, 2012
  12. 12. exponential cost increase
  13. 13. store only differences
  14. 14. work on compressed data
  15. 15. sublinear scaling
  16. 16. new algorithms needed
  17. 17. network biology
  18. 18. beyond pathways
  19. 19. no discoveries
  20. 20. protein networks
  21. 21. STRING
  22. 22. Franceschini et al., Nucleic Acids Research, 2013
  23. 23. regulation
  24. 24. cell cycle
  25. 25. de Lichtenberg, Jensen et al., Science, 2005
  26. 26. disease phenotypes
  27. 27. heart development
  28. 28. Lage et al., Molecular Systems Biology, 2010
  29. 29. diabetes
  30. 30. Bergholdt et al., Diabetes, 2013
  31. 31. applied text mining
  32. 32. biomedical literature
  33. 33. >10 km
  34. 34. too much to read
  35. 35. full-text articles
  36. 36. electronic health records
  37. 37. structured data
  38. 38. Jensen et al., Nature Reviews Genetics, 2012
  39. 39. unstructured data
  40. 40. temporal correlations
  41. 41. diagnosis trajectories
  42. 42. Jensen et al., submitted, 2013
  43. 43. adverse drug reactions
  44. 44. Eriksson et al., submitted, 2013
  45. 45. industry needs
  46. 46. goals
  47. 47. biomarker discovery
  48. 48. target discovery
  49. 49. ADR prediction
  50. 50. data sources
  51. 51. in-house data
  52. 52. expression profiles
  53. 53. screening assays
  54. 54. security
  55. 55. legal agreements
  56. 56. poorly standardized
  57. 57. public data
  58. 58. expression profiles
  59. 59. interaction networks
  60. 60. literature
  61. 61. unawareness
  62. 62. patient data
  63. 63. security
  64. 64. privacy
  65. 65. core competencies
  66. 66. biological data mining
  67. 67. quantity is not the problem
  68. 68. many data types
  69. 69. different formats
  70. 70. different identifiers
  71. 71. different quality
  72. 72. scripting language
  73. 73. biological understanding
  74. 74. statistical mindset
  75. 75. medical text mining
  76. 76. free text
  77. 77. in Norwegian
  78. 78. by busy doctors
  79. 79. text-mining techniques
  80. 80. information extraction
  81. 81. named entity recognition
  82. 82. dictionary construction
  83. 83. medical knowledge
  84. 84. speak Norwegian
  85. 85. summary
  86. 86. custom solution
  87. 87. no standard software
  88. 88. interdisciplinary team
  89. 89. outsourcing

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