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

应对药物发现中的“系统”挑战：分析可积贝叶斯方法。

• 批准号: 1668568
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1668568
• 关键词: Meeting; systems; challenges; drug; discovery

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.

医学上常用的统计方法并没有跟上实验的进步。这导致临床预测不准确、患者分层不良、医学试验不必要的失败以及错失临床机会。在这个项目中，我们开发了用于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分析而不是数值来完成，以便这些方法可以系统地应用于高维（例如基因组）医学数据。新方法将在伦敦国王学院开发，并应用于葛兰素史克正在进行的系统药理学研究问题。

项目成果

Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation

无需超参数交叉验证即可进行准确的贝叶斯数据分类

• DOI: 10.1007/s00357-019-09316-6
• 发表时间: 2019
• 期刊: Journal of Classification
• 影响因子: 2
• 作者: Sheikh M

Replica analysis of overfitting in generalized linear regression models

• DOI: 10.1088/1751-8121/aba028
• 发表时间: 2020-09-11
• 期刊: JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
• 影响因子: 2.1
• 作者: Coolen, A. C. C.;Sheikh, M.;Antenucci, F.
• 通讯作者: Antenucci, F.

Analysis of overfitting in the regularized Cox model

• DOI: 10.1088/1751-8121/ab375c
• 发表时间: 2019-09-20
• 期刊: JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
• 影响因子: 2.1
• 作者: Sheikh, Mansoor;Coolen, Anthony C. C.
• 通讯作者: Coolen, Anthony C. C.