STRING - Large-scale integration of data and text
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This document discusses large-scale integration of biological data and text. It mentions combining data from many databases on proteins, interactions, complexes and pathways using parsers and mapping files to overcome different formats and identifiers. It discusses using techniques like co-mentioning within documents, paragraphs and sentences to provide comprehensive information and improve quality scores. The goal is to combine all available evidence from various sources to generate a comprehensive resource, as described on the string-db.org website and Cytoscape app.
STRING - Large-scale integration of data and text
- 1. STRING Large-scale integration of data and text Lars Juhl Jensen
- 7. gene fusion
- 8. Korbel et al., Nature Biotechnology, 2004
- 10. Korbel et al., Nature Biotechnology, 2004
- 15. Jensen & Bork, Science, 2008
- 19. pathways
- 20. Letunic & Bork, Trends in Biochemical Sciences, 2008
- 21. many databases
- 24. variable quality
- 25. not comparable
- 26. hard work
- 27. parsers
- 28. mapping files
- 29. quality scores
- 31. von Mering et al., Nucleic Acids Research, 2005
- 33. gold standard
- 34. von Mering et al., Nucleic Acids Research, 2005
- 35. implicit weighting by quality
- 36. common scale
- 39. Franceschini et al., Nucleic Acids Research, 2013
- 40. missing most of the data
- 41. >10 km
- 42. too much to read
- 43. computer
- 44. as smart as a dog
- 45. teach it specific tricks
- 50. cyclin dependent kinase 1
- 51. CDC2
- 54. cyclin dependent kinase 1
- 57. CDC2
- 58. hCdc2
- 59. “black list”
- 60. SDS
- 61. co-mentioning
- 62. counting
- 63. within documents
- 65. within sentences
- 66. quality scores
- 70. Szklarczyk et al., Nucleic Acids Research, 2015string-db.org
- 71. web resource
- 72. Cytoscape App
- 75. Acknowledgments Damian Szklarczyk Michael Kuhn Andrea Franceschini Milan Simonovic Alexander Roth Sune Pletscher-Frankild John “Scooter” Morris Christian von Mering Peer Bork