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Turning literature into databases
1.
Turning literature into
databases >10 km Lars Juhl Jensen
2.
corpora
3.
22M abstracts
4.
1.9M freely available
articles
5.
1.9M Elsevier documents
6.
entity recognition
7.
identify the concepts
8.
comprehensive lexicon
9.
small molecules
10.
proteins
11.
cellular components
12.
tissues
13.
organisms
14.
phenotypes
15.
diseases
16.
orthographic variation
17.
singular vs. plural
18.
flexible matching
19.
spaces and hyphens
20.
“black list”
21.
information extraction
22.
count co-mentioning
23.
within documents
24.
within paragraphs
25.
within sentences
26.
new scoring scheme
27.
28.
29.
STRING v9.1
30.
~2x better sensitivity
31.
web-centric databases
32.
suite of web
interfaces
33.
common backend database
34.
diseases.jensenlab.org
35.
search for a
protein
36.
ranked table of
diseases
37.
38.
search for a
disease
39.
STRING network
40.
41.
evidence viewer
42.
43.
compartments.jensenlab.org
44.
text mining
45.
curated knowledge
46.
sequence-based predictions
47.
visualization
48.
49.
tissues.jensenlab.org
50.
51.
related projects
52.
importance of full
text
53.
NIH grant abstracts
54.
55.
electronic patient records
56.
patient stratification
57.
Roque et al.,
PLoS Computational Biology, 2011
58.
pharmacovigilance
59.
Eriksson et al.,
in preparation, 2012
60.
Thank you!
Sune Frankild Janos Binder Kalliopi Tsafou Peter Bjødstrup Jensen Robert Eriksson
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