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SUMMARY:Combining molecular and physiological data from complex psychiatri
 c disorders - Dr. Pietro Lio / Emanuel Schwarz (Computer Laboratory / Depa
 rtment of Chemical Engineering and Biotechnology)
DTSTART:20081023T092000Z
DTEND:20081023T094500Z
UID:TALK13225@talks.cam.ac.uk
CONTACT:Duncan Simpson
DESCRIPTION:Human diseases result from abnormalities in an extremely compl
 ex system of molecular processes. In these processes\, virtually no molecu
 lar entity acts in isolation and complexity is caused by the vast amount o
 f dependencies between molecular and phenotypological features. It is a ve
 ry intuitive concept to represent such complex information in the form of 
 networks. Different layers of networks can describe the dependency structu
 res between patients\, genes\, proteins and\, ultimately\, diseases. These
  data-types often arise from different sources and their integration is ur
 gently needed to obtain a better understanding of complex disease processe
 s. Here we developed a graph theoretical framework for combining and untan
 gling the relationship of physiological and molecular data through the exp
 loration of the dependency structure between disease-related network layer
 s. We describe how this network medicine approach may lead to more accurat
 e diagnosis and a more comprehensive knowledge of pathological mechanisms.
  We demonstrate the methodology using a clinical dataset of patients suffe
 ring from schizophrenia\, affective disorder and healthy volunteers. In a 
 second example we show how complex graph theoretic approaches can also be 
 used to describe other important physiological processes such as the perce
 ption of time and space
LOCATION:Kaetsu Centre\, New Hall
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