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Making gene networks through data integration
1.
Making gene networks
through data integration Lars Juhl Jensen
2.
association networks
3.
guilt by association
4.
5.
molecular networks
6.
proteins
7.
string-db.org
8.
small molecules
9.
stitch-db.org
10.
non-coding RNAs
11.
compartments
12.
compartments.jensenlab.org
13.
tissues
14.
tissues.jensenlab.org
15.
diseases
16.
data integration
17.
computational predictions
18.
gene neighborhood
19.
Korbel et al.,
Nature Biotechnology, 2004
20.
TargetScan
21.
experimental data
22.
gene expression
23.
24.
protein interactions
25.
Jensen & Bork,
Science, 2008
26.
miRTarBase
27.
curated knowledge
28.
metabolic pathways
29.
Letunic & Bork,
Trends in Biochemical Sciences, 2008
30.
signaling pathways
31.
many databases
32.
different formats
33.
different identifiers
34.
variable quality
35.
not comparable
36.
hard work
37.
(Ph.D. students)
38.
common identifiers
39.
quality scores
40.
von Mering et
al., Nucleic Acids Research, 2005
41.
score calibration
42.
von Mering et
al., Nucleic Acids Research, 2005
43.
homology-based transfer
44.
Franceschini et al.,
Nucleic Acids Research, 2013
45.
missing most of
the data
46.
text mining
47.
>10 km
48.
too much to
read
49.
computer
50.
as smart as
a dog
51.
teach it specific
tricks
52.
53.
54.
named entity recognition
55.
comprehensive lexicon
56.
let-7a-3p
57.
let-7a*
58.
flexible matching
59.
let-7a
60.
let7a
61.
name expansions
62.
let-7a
63.
miR-let-7a
64.
“black list”
65.
SDS
66.
co-mentioning
67.
counting
68.
within documents
69.
within paragraphs
70.
within sentences
71.
72.
73.
high recall
74.
high precision
75.
fuzzy associations
76.
NLP Natural Language Processing
77.
Gene and protein
names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]
78.
extract stated facts
79.
high precision
80.
poor recall
81.
Jensen et al.,
Nature Reviews Genetics, 2006
82.
questions?
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