Meeting the 'systems' challenges in drug discovery: Analytically Integrable Bayesian Methods.

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The statistical methods commonly used in medicine have not kept pace with experimental advances. This leads to inaccurate clinical predictions, poor patient stratification, unnecessary failures of medical trials, and missed clinical opportunities. In this project we develop Bayesian methods for discriminant analysis in which the relevant high-dimensional parameter integrals can be done analytically as opposed to numerically, so that such methods can be applied systematically to high-dimensional (e.g. genomic) medical data. The new methodology will be developed at King's College London, and be applied to ongoing research problems in systems pharmacology at GSK.