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

医学上常用的统计方法跟不上实验的进步。这导致不准确的临床预测、糟糕的患者分层、不必要的医学试验失败和错失临床机会。在这个项目中，我们开发了判别分析的贝叶斯方法，其中相关的高维参数积分可以通过解析而不是数值来完成，因此这种方法可以系统地应用于高维(例如基因组)医学数据。伦敦国王学院将开发新的方法，并将其应用于葛兰素史克正在进行的系统药理学研究问题。

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.