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Text and data mining

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Lars Juhl Jensen
Lars Juhl Jensen
TecnologiaEducação
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Text and data mining Lars Juhl Jensen
Part 1 text mining
exponential growth
 
 
some things are constant
 
~45 seconds per paper
computer
as smart as a dog
teach it specific tricks
 
 
named entity identification
Reflect
Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009
comprehensive lexicon
orthographic variation
“ black list”
information extraction
no access
 
collaboration
 
 
Part 2 protein networks
guilt by association
 
STRING
Szklarczyk, Franceschini et al., Nucleic Acids Research , 2011
genomic context
gene fusion
Korbel et al., Nature Biotechnology , 2004
experimental data
physical interactions
Jensen & Bork, Science , 2008
gene coexpression
genetic interactions
Beyer et al., Nature Reviews Genetics , 2007
 
curated knowledge
pathways
Letunic & Bork, Trends in Biochemical Sciences , 2008
text mining
 
many data types
many databases
different formats
different identifiers
variable quality
quality scores
calibrate vs. gold standard
von Mering et al., Nucleic Acids Research , 2005
orthology transfer
Frishman et al., Modern Genome Annotation , 2009
Part 3 drug networks
new uses for old drugs
shared target(s)
chemical similarity
Campillos & Kuhn et al., Science , 2008
similar drugs share targets
Campillos & Kuhn et al., Science , 2008
only trivial predictions
phenotypic similarity
chemical perturbations
phenotypic readouts
drug treatment
side effects
no database
package inserts
Campillos & Kuhn et al., Science , 2008
text mining
manual validation
side-effect correlations
Campillos & Kuhn et al., Science , 2008
side-effect frequencies
Campillos & Kuhn et al., Science , 2008
side-effect similarity
chemical similarity
Campillos & Kuhn et al., Science , 2008
categorization
Campillos & Kuhn et al., Science , 2008
20 drug–drug pairs
in vitro binding assays
K i <10 µM for 11 of 20
cell assays
9 of 9 showed activity
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larsjuhljensen
 

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