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Scalable network modelling for personalized medicine

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If you have a question about this talk, please contact Dr Jack Bowden.

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 context (including genetic background) or change in response to therapy. Understanding 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 networks from large numbers of samples. I will talk about our ongoing efforts to develop statistical approaches for this setting, focusing in particular on cancer and protein signalling networks. I will discuss Bayesian approaches for network inference from time-course data; the use of prior knowledge; and systematic experimental validation of networks. Finally, I will touch upon some future perspectives for statistical research in personalized medicine, as well as some of the key challenges and caveats.

This talk is part of the MRC Biostatistics Unit Seminars series.

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