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Combining molecular and physiological data from complex psychiatric disorders

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  • UserDr. Pietro Lio / Emanuel Schwarz (Computer Laboratory / Department of Chemical Engineering and Biotechnology)
  • ClockThursday 23 October 2008, 10:20-10:45
  • HouseKaetsu Centre, New Hall.

If you have a question about this talk, please contact Duncan Simpson.

Human diseases result from abnormalities in an extremely complex system of molecular processes. In these processes, virtually no molecular entity acts in isolation and complexity is caused by the vast amount of dependencies between molecular and phenotypological features. It is a very intuitive concept to represent such complex information in the form of networks. Different layers of networks can describe the dependency structures between patients, genes, proteins and, ultimately, diseases. These data-types often arise from different sources and their integration is urgently needed to obtain a better understanding of complex disease processes. Here we developed a graph theoretical framework for combining and untangling the relationship of physiological and molecular data through the exploration of the dependency structure between disease-related network layers. We describe how this network medicine approach may lead to more accurate diagnosis and a more comprehensive knowledge of pathological mechanisms. We demonstrate the methodology using a clinical dataset of patients suffering 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 perception of time and space

This talk is part of the Networks & Neuroscience series.

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