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Network medicine - Integrating drugs, targets, diseases and side-effects
 

Network medicine - Integrating drugs, targets, diseases and side-effects

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LEO Pharma, Ballerup, Denmark, November 5, 2008

LEO Pharma, Ballerup, Denmark, November 5, 2008

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    Network medicine - Integrating drugs, targets, diseases and side-effects Network medicine - Integrating drugs, targets, diseases and side-effects Presentation Transcript

    • Lars Juhl Jensen Network medicine Integrating drugs, targets, diseases and side-effects
    • Lars Juhl Jensen Network medicine Integrating drugs, targets, diseases and side-effects
    • Lars Juhl Jensen Network medicine Integrating drugs, targets, diseases and side-effects
    •  
    •  
    • what went wrong?
    • phase 0
    • before you do anything
    • a good problem
    • a good idea
    • dynamics of complexes
    • microarray data
    • protein interactions
    • de Lichtenberg, Jensen, et al., Science , 2005
    • phosphorylation networks
    • mass spectrometry data
    • sequence motifs
    • functional associations
    • Linding, Jensen, Ostheimer et al., Cell , 2007
    • Linding, Jensen, Ostheimer et al., Cell , 2007
    • the problem
    • new uses for old drugs
    • drug–drug network
    • shared target(s)
    • chemical similarity
    • Tanimoto coefficients
    •  
    •  
    • similar drugs share targets
    • only trivial predictions
    • the idea
    • chemical perturbations
    • phenotypic readouts
    • drug treatment
    • side effects
    • the implementation
    • information on side effects
    • package inserts
    •  
    • text mining
    • side-effect ontology
    • backtracking
    •  
    • side-effect correlations
    •  
    • GSC weighting
    • side-effect frequencies
    •  
    • raw similarity score
    •  
    • p-values
    •  
    • side-effect similarity
    • chemical similarity
    •  
    • reference set
    • drug–target pairs
    •  
    • drug–drug pairs
    • score bins
    • benchmark
    •  
    • fit calibration function
    •  
    • probabilistic scores
    • the results
    • drug–drug network
    • ATC codes
    •  
    • categorization
    •  
    •  
    •  
    • map onto score space
    •  
    • the experiments
    • 20 drug–drug relations
    • in vitro binding assays
    •  
    •  
    •  
    • K i <10 µM for 11 of 20
    • cell assays
    • 9 of 9 showed activity
    •  
    • the big picture
    • STITCH
    •  
    • protein–chemical network
    •  
    • primary experimental data
    • activity screens
    • Fedorov et al., PNAS , 2007
    • protein interactions
    • Jensen & Bork, Science , 2008
    • gene coexpression
    •  
    • genomic context
    • Korbel et al., Nature Biotechnology , 2004
    • literature mining
    •  
    • curated knowledge
    • Letunic & Bork, Trends in Biochemical Sciences , 2008
    • different formats
    • different identifiers
    • different reliability
    • benchmarking
    • von Mering et al., Nucleic Acids Research , 2005
    • 373 genomes
    • Jensen et al., Nucleic Acids Research , 2008
    • transfer by orthology
    • combine all evidence
    •  
    • Acknowledgments
      • Monica Campillos
      • Michael Kuhn
      • Christian von Mering
      • Anne-Claude Gavin
      • Peer Bork
    • http://larsjuhljensen.wordpress.com