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Statistical modelling of networks in disease biology

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An emerging approach in systems biology and personalized medicine is that of relating molecular networks to disease outcomes and treatment. In a nutshell, the idea is that networks that describe molecular interplay relevant to disease biology may differ between patients, or between patient subpopulations, such that their systematic characterization could help to explain corresponding variation in disease phenotypes or response to therapy. A major challenge is to develop experimental protocols and computational tools by which to elucidate such networks and explore their heterogeneity across patients. I will describe our ongoing efforts to develop statistical approaches for this setting including: methods for network estimation from time-course and snapshot data, using linear and nonlinear models; exploration of network heterogeneity; and systematic experimental validation. Along the way I will highlight some of the opportunities and challenges for statistical research in this area, as well as some of the caveats and concerns.

This talk is part of the Statistics series.

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