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Lars Juhl Jensen
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Danish Protein Science Meeting, Skt. Helene, Tisvildeleje, Denmark, November 6-7, 2008
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Recommandé
Unraveling cellular phosphorylation networks using computational biology
Unraveling cellular phosphorylation networks using computational biology
Lars Juhl Jensen
Unraveling signaling networks by large-scale data integration
Unraveling signaling networks by large-scale data integration
Lars Juhl Jensen
Combining sequence motifs and protein interactions to unravel complex phospho...
Combining sequence motifs and protein interactions to unravel complex phospho...
Lars Juhl Jensen
Unraveling signaling networks by data integration
Unraveling signaling networks by data integration
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Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and text
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Network biology - Large-scale integration of data and text
Network biology - Large-scale integration of data and text
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Cross-species data integration
Cross-species data integration
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Gene association networks - Large-scale integration of data and text
Gene association networks - Large-scale integration of data and text
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Recommandé
Unraveling cellular phosphorylation networks using computational biology
Unraveling cellular phosphorylation networks using computational biology
Lars Juhl Jensen
Unraveling signaling networks by large-scale data integration
Unraveling signaling networks by large-scale data integration
Lars Juhl Jensen
Combining sequence motifs and protein interactions to unravel complex phospho...
Combining sequence motifs and protein interactions to unravel complex phospho...
Lars Juhl Jensen
Unraveling signaling networks by data integration
Unraveling signaling networks by data integration
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Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and text
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Network biology - Large-scale integration of data and text
Network biology - Large-scale integration of data and text
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Cross-species data integration
Cross-species data integration
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Gene association networks - Large-scale integration of data and text
Gene association networks - Large-scale integration of data and text
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Network biology: Large-scale biomedical data and text mining
Network biology: Large-scale biomedical data and text mining
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One tagger, many uses - Illustrating the power of ontologies in named entity ...
One tagger, many uses - Illustrating the power of ontologies in named entity ...
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STRING - Protein networks from data and text mining
STRING - Protein networks from data and text mining
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Introduction to STRING
Introduction to STRING
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Networks of proteins and diseases
Networks of proteins and diseases
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Gene Association Networks: Large-scale integration of data and text
Gene Association Networks: Large-scale integration of data and text
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Gene association networks - Large-scale integration of data and text
Gene association networks - Large-scale integration of data and text
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Data and Text Mining
Data and Text Mining
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Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and text
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Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and text
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Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and text
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STRING: Protein networks from data and text mining
STRING: Protein networks from data and text mining
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Network biology
Network biology
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Systems biology: Bioinformatics on complete biological system
Systems biology: Bioinformatics on complete biological system
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Networks of proteins and diseases
Networks of proteins and diseases
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Data integration - Integration of functional associations using STRING
Data integration - Integration of functional associations using STRING
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Journal Club 2013-09-10: Pandya et al
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Network Biology: A crash course on STRING and Cytoscape
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Visick Seminar Presentation
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Network biology: Large-scale biomedical data and text mining
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STRING - Protein networks from data and text mining
STRING - Protein networks from data and text mining
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Introduction to STRING
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Networks of proteins and diseases
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Gene Association Networks: Large-scale integration of data and text
Gene Association Networks: Large-scale integration of data and text
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Gene association networks - Large-scale integration of data and text
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Data and Text Mining
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Gene association networks: Large-scale integration of data and text
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Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and text
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Gene association networks: Large-scale integration of data and text
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STRING: Protein networks from data and text mining
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Network biology
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Systems biology: Bioinformatics on complete biological system
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Networks of proteins and diseases
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Data integration - Integration of functional associations using STRING
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STRING - Protein networks from data and text mining
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Gene Association Networks: Large-scale integration of data and text
Gene Association Networks: Large-scale integration of data and text
Gene association networks - Large-scale integration of data and text
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Data and Text Mining
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Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and text
Gene association networks: Large-scale integration of data and text
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Gene association networks: Large-scale integration of data and text
STRING: Protein networks from data and text mining
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Network biology
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Systems biology: Bioinformatics on complete biological system
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Data integration - Integration of functional associations using STRING
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Journal Club 2013-09-10: Pandya et al
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Visick Seminar Presentation
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Systems Biology Systems
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Computational Biology - Signaling networks and drug repositioning
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Cellular network biology: Proteome-wide analysis of heterogeneous data
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Unraveling signaling networks by large-scale data integration
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Plus de Lars Juhl Jensen
One tagger, many uses: Illustrating the power of dictionary-based named entit...
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One tagger, many uses: Simple text-mining strategies for biomedicine
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Extract 2.0: Text-mining-assisted interactive annotation
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Biomedical text mining: Automatic processing of unstructured text
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Cellular networks
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Cellular Network Biology
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Biomarker bioinformatics: Network-based candidate prioritization
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The Art of Counting: Scoring and ranking co-occurrences in literature
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Network visualization: A crash course on using Cytoscape
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Biomedical text mining: Automatic processing of unstructured text
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Cellular networks
Cellular networks
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Cellular Network Biology: Large-scale integration of data and text
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Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactions
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
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Cellular Network Biology
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Biomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritization
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Data Integration and Systems Biology
1.
Lars Juhl Jensen
Data Integration and Systems Biology
2.
Lars Juhl Jensen
Data Integration and Systems Biology
3.
what went wrong?
4.
a good problem
5.
a good idea
6.
cell-cycle regulation
7.
microarray data
8.
interaction network
9.
de Lichtenberg, Jensen,
et al., Science , 2005
10.
new uses for
old drugs
11.
chemical similarity
12.
side effects
13.
14.
15.
16.
the problem
17.
phosphorylation networks
18.
mass spectrometry
19.
Linding, Jensen, Ostheimer
et al., Cell , 2007
20.
phosphorylation sites
21.
in vivo
22.
kinases are unknown
23.
peptide assays
24.
Miller, Jensen et
al., Science Signaling , 2008
25.
sequence specificity
26.
kinase-specific
27.
in vitro
28.
no context
29.
what a kinase
could do
30.
not what it
actually does
31.
computational methods
32.
sequence specificity
33.
Miller, Jensen et
al., Science Signaling , 2008
34.
kinase-specific
35.
no context
36.
what a kinase
could do
37.
not what it
actually does
38.
in vitro
39.
in vivo
40.
context
41.
co-activators
42.
scaffolders
43.
expression
44.
association networks
45.
Linding, Jensen, Ostheimer
et al., Cell , 2007
46.
the idea
47.
mass spectrometry
48.
phosphorylation sites
49.
sequence motifs
50.
sequence specificity
51.
association network
52.
context
53.
in vitro
54.
in vivo
55.
Linding, Jensen, Ostheimer
et al., Cell , 2007
56.
the sequence motifs
57.
NetPhorest
58.
Miller, Jensen et
al., Science Signaling , 2008
59.
pipeline
60.
data organization
61.
Miller, Jensen et
al., Science Signaling , 2008
62.
compilation of datasets
63.
Miller, Jensen et
al., Science Signaling , 2008
64.
redundancy reduction
65.
Miller, Jensen et
al., Science Signaling , 2008
66.
cross-validation partitioning
67.
Miller, Jensen et
al., Science Signaling , 2008
68.
training and evaluation
69.
classifier selection
70.
Miller, Jensen et
al., Science Signaling , 2008
71.
motif atlas
72.
73.
179 kinases
74.
93 SH2 domains
75.
8 PTB domains
76.
BRCT domains
77.
WW domains
78.
14-3-3 proteins
79.
benchmarking
80.
Miller, Jensen et
al., Science Signaling , 2008
81.
low-specificity kinases
82.
disease-related kinases
83.
Miller, Jensen et
al., Science Signaling , 2008
84.
docking domains
85.
Miller, Jensen et
al., Science Signaling , 2008
86.
the context network
87.
STRING
88.
functional associations
89.
373 genomes
90.
Jensen et al.,
Nucleic Acids Research , 2008
91.
genomic context methods
92.
gene fusion
93.
Korbel et al.,
Nature Biotechnology , 2004
94.
conserved neighborhood
95.
Korbel et al.,
Nature Biotechnology , 2004
96.
phylogenetic profiles
97.
Korbel et al.,
Nature Biotechnology , 2004
98.
primary experimental data
99.
protein interactions
100.
Jensen & Bork,
Science , 2008
101.
gene coexpression
102.
103.
literature mining
104.
105.
curated knowledge
106.
Letunic & Bork,
Trends in Biochemical Sciences , 2008
107.
different formats
108.
parsers
109.
different gene identifiers
110.
thesaurus
111.
redundancy
112.
bookkeeping
113.
variable reliability
114.
benchmarking
115.
von Mering et
al., Nucleic Acids Research , 2005
116.
spread over many
species
117.
transfer by orthology
118.
von Mering et
al., Nucleic Acids Research , 2005
119.
combine all evidence
120.
Linding, Jensen, Ostheimer
et al., Cell , 2007
121.
the results
122.
NetworKIN
123.
123 kinases
124.
5515 substrates
125.
21,702 sites
126.
benchmarking
127.
Phospho.ELM
128.
Linding, Jensen, Ostheimer
et al., Cell , 2007
129.
2.5-fold better accuracy
130.
context is crucial
131.
ATM signaling
132.
Linding, Jensen, Ostheimer
et al., Cell , 2007
133.
Linding, Jensen, Ostheimer
et al., Cell , 2007
134.
small-scale validation
135.
ATM phosphorylates Rad50
136.
Linding, Jensen, Ostheimer
et al., Cell , 2007
137.
high-throughput validation
138.
multiple reaction monitoring
139.
Linding, Jensen, Ostheimer
et al., Cell , 2007
140.
systematic validation
141.
kinase inhibitor matrix
142.
Fedorov et al.,
PNAS , 2007
143.
design optimal experiments
144.
145.
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