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SUMMARY:Scalable network modelling for personalized medicine - Sach Mukher
 jee\, Netherlands Cancer Institute (NKI)
DTSTART:20130423T130000Z
DTEND:20130423T140000Z
UID:TALK44685@talks.cam.ac.uk
CONTACT:Dr Jack Bowden
DESCRIPTION:Causal relationships between molecular entities\, as described
  by networks\, play a key role in the biology of many diseases\, including
  cancer. It is becoming clear that such networks may vary depending on con
 text (including genetic background) or change in response to therapy. Unde
 rstanding such variation may be crucial to developing useful predictors of
  therapeutic response. However\, doing so requires biochemical assays and 
 inference approaches that scale up to permit estimation of meaningful netw
 orks from large numbers of samples. I will talk about our ongoing efforts 
 to develop statistical approaches for this setting\, focusing in particula
 r on cancer and protein signalling networks. I will discuss Bayesian appro
 aches for network inference from time-course data\; the use of prior knowl
 edge\; and systematic experimental validation of networks. Finally\, I wil
 l touch upon some future perspectives for statistical research in personal
 ized medicine\, as well as some of the key challenges and caveats.
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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