Network biology: Large-scale data and text mining
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This document discusses network biology and large-scale text mining. It describes using computational predictions, experimental data, and text mining to build protein interaction networks for various species from databases with different formats and quality. It also discusses using named entity recognition, expansion rules, and flexible matching to extract information from millions of abstracts and articles to identify relationships between biological entities like proteins, complexes, pathways, tissues, compartments, and diseases. The extracted information is integrated into web interfaces and services to allow visualization and exploration of the biological networks and relationships.
Network biology: Large-scale data and text mining
- 1. Network biology Large-scale data and text mining Lars Juhl Jensen
- 5. STRING
- 7. gene fusion
- 8. Korbel et al., Nature Biotechnology, 2004
- 10. Korbel et al., Nature Biotechnology, 2004
- 12. Korbel et al., Nature Biotechnology, 2004
- 17. Jensen & Bork, Science, 2008
- 19. complexes
- 20. pathways
- 21. Letunic & Bork, Trends in Biochemical Sciences, 2008
- 22. many databases
- 25. variable quality
- 26. not comparable
- 27. not same species
- 28. hard work
- 29. quality scores
- 30. von Mering et al., Nucleic Acids Research, 2005
- 31. calibrate vs. gold standard
- 32. von Mering et al., Nucleic Acids Research, 2005
- 34. Franceschini et al., Nucleic Acids Research, 2013
- 35. missing most of the data
- 36. text mining
- 37. >10 km
- 38. too much to read
- 39. computer
- 40. as smart as a dog
- 41. teach it specific tricks
- 46. CDC2
- 47. cyclin dependent kinase 1
- 48. expansion rules
- 49. hCdc2
- 50. CDC2
- 53. cyclin dependent kinase 1
- 54. “black list”
- 55. SDS
- 57. Reflect
- 58. browser add-on
- 60. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010
- 62. co-mentioning
- 63. within documents
- 65. within sentences
- 66. text corpus
- 68. no access
- 69. millions of full-text articles
- 72. general approach
- 73. COMPARTMENTS
- 74. TISSUES
- 75. DISEASES
- 78. text mining
- 81. quality scores
- 82. visualization
- 85. dissemination
- 86. web interfaces
- 88. web services
- 90. bulk download
- 91. 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