Data and Text Mining
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The document discusses Lars Juhl Jensen's work in data and text mining of biomedical literature and records to analyze protein networks, gene interactions, and predict relationships between genes and proteins. Jensen uses text mining techniques like named entity recognition and information extraction from millions of abstracts and articles to build resources on protein interactions, gene neighborhoods, and disease localization that are compiled on websites for public use and dissemination of the knowledge.
Data and Text Mining
- 1. Data and Text Mining Lars Juhl Jensen
- 8. de Lichtenberg, Jensen et al., Science, 2005
- 10. Campillos, Kuhn et al., Science, 2008
- 12. group leader
- 14. cofounder
- 16. data mining
- 17. proteomics
- 18. text mining
- 21. protein networks
- 24. STRING
- 25. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
- 27. gene fusion
- 28. Korbel et al., Nature Biotechnology, 2004
- 30. operons
- 31. Korbel et al., Nature Biotechnology, 2004
- 33. Korbel et al., Nature Biotechnology, 2004
- 35. Korbel et al., Nature Biotechnology, 2004
- 36. a real example
- 45. Jensen & Bork, Science, 2008
- 47. Beyer et al., Nature Reviews Genetics, 2007
- 49. complexes
- 50. pathways
- 51. Letunic & Bork, Trends in Biochemical Sciences, 2008
- 52. many databases
- 55. variable quality
- 56. not comparable
- 57. not same species
- 58. hard work
- 59. quality scores
- 60. von Mering et al., Nucleic Acids Research, 2005
- 61. calibrate vs. gold standard
- 62. von Mering et al., Nucleic Acids Research, 2005
- 64. Franceschini et al., Nucleic Acids Research, 2013
- 65. missing most of the data
- 66. text mining
- 67. >10 km
- 68. too much to read
- 69. computer
- 70. as smart as a dog
- 71. teach it specific tricks
- 76. CDC2
- 77. cyclin dependent kinase 1
- 78. expansion rules
- 79. hCdc2
- 80. CDC2
- 83. cyclin dependent kinase 1
- 84. “black list”
- 85. SDS
- 87. Reflect
- 88. browser add-on
- 90. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010
- 92. co-mentioning
- 93. within documents
- 95. within sentences
- 96. text corpus
- 98. no access
- 99. millions of full-text articles
- 102. general approach
- 103. COMPARTMENTS
- 104. TISSUES
- 105. DISEASES
- 106. curated knowledge
- 107. experimental data
- 108. text mining
- 110. common identifiers
- 111. quality scores
- 112. visualization
- 115. dissemination
- 116. web interfaces
- 118. web services
- 120. bulk download
- 121. 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